Paper list for broad topics in machine learning systems

NOTE: Survey papers are annotated with [Survey 🔍] prefix.

• Paper List for Machine Learning Systems
  • Table of Contents
  • Data Processing
    • Data pipeline optimization
    • Caching and distributed storage for ML training
    • LLM data plane
    • Others
  • Training System
    • ML job analysis on GPU clusters
    • Resource scheduling
    • Distributed training
    • AutoML
    • GNN training system
  • Inference System
  • Attention Optimization
  • Mixture of Experts (MoE)
  • Communication Optimization & Network Infrastructure for Distributed ML
  • Fault tolerance & Straggler mitigation
  • GPU Memory Management & Optimization
  • GPU Sharing
  • Compiler
  • GPU Kernel Optimization
  • LLM Long Context
  • Model Compression
  • Federated Learning
  • Privacy-Preserving ML
  • ML APIs & Application-Side Optimization
  • ML for Systems
  • Energy Efficiency
  • Retrieval-Augmented Generation (RAG)
  • Simulation
  • Systems for Agentic AI
  • RL Post-Training
  • Multimodal
  • Hybrid LLMs
  • Others
• References

General

• [arxiv'25] Scalable and Performant Data Loading
• [arxiv'25] OVERLORD: Ultimate Scaling of DataLoader for Multi-Source Large Foundation Model Training
• [arxiv'25] The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution
• [arxiv'25] In-Network Preprocessing of Recommender Systems on Multi-Tenant SmartNICs
• [VLDB'25] cedar: Composable and Optimized Machine Learning Input Data Pipelines
• [HotInfra'24] Lotus: Characterize Architecture Level CPU-based Preprocessing in Machine Learning Pipelines
• [arxiv'24] TensorSocket: Shared Data Loading for Deep Learning Training
• [arxiv'24] Efficient Tabular Data Preprocessing of ML Pipelines
• [MLSys'22] Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
• [ISCA'22] Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
• [SIGMOD'22] Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines
• [VLDB'21] Analyzing and Mitigating Data Stalls in DNN Training
• [VLDB'21] tf.data: A Machine Learning Data Processing Framework

Preprocessing stalls

• [arxiv'24] PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers
• [ATC'24] Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement
• [HotStorage'24] A Selective Preprocessing Offloading Framework for Reducing Data Traffic in DL Training
• [VLDB'24] FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation
• [arxiv'23] Rinas: Training with Dataset Shuffling Can Be General and Fast
• [CVPR'23] FFCV: Accelerating Training by Removing Data Bottlenecks
• [RecSys'23] InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• [SIGMOD'23] GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning
• [VLDB'23] FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline
• [SoCC'23] tf.data service: A Case for Disaggregating ML Input Data Processing
  • arxiv version
• [ATC'22] Cachew: Machine Learning Input Data Processing as a Service
• [OSDI'22] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters
• [ICPP'19] DLBooster: Boosting End-to-End Deep Learning Workflows with Offloading Data Preprocessing Pipelines

Fetch stalls (I/O)

• [TACO'23] Fastensor: Optimise the Tensor I/O Path from SSD to GPU for Deep Learning Training
• [ICPP'22] Lobster: Load Balance-Aware I/O for Distributed DNN Training
• [SC'21] Clairvoyant Prefetching for Distributed Machine Learning I/O

Specific workloads (GNN, DLRM)

• [VLDB'25] Eliminating Data Processing Bottlenecks in GNN Training over Large Graphs via Two-level Feature Compression
• [ISCA'24] PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models
• [arxiv'23] Towards Data-centric Graph Machine Learning: Review and Outlook
• [arxiv'23] FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
• [MLSys'23] RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
• [ASPLOS'22] RecShard: statistical feature-based memory optimization for industry-scale neural recommendation
• [RecSys'23] InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• [arxiv'23] MTrainS: Improving DLRM training efficiency using heterogeneous memories
• [SOSP'23] Bagpipe: Accelerating Deep Recommendation Model Training
• [SOSP'23] gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning
• [NSDI'23] BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
• [DAC'22] A Joint Management Middleware to Improve Training Performance of Deep Recommendation Systems with SSDs
• [VLDB'22] Accelerating Recommendation System Training by Leveraging Popular Choices

• [ATC'25] HyCache: Hybrid Caching for Accelerating DNN Input Preprocessing Pipelines
• [ICDE'25] MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage
• [TPDS'23] High-Level Data Abstraction and Elastic Data Caching for Data-Intensive AI Applications on Cloud-Native Platforms
• [SOSP'23] UGACHE: A Unified GPU Cache for Embedding-based Deep Learning
• [ATC'23] Tectonic-Shift: A Composite Storage Fabric for Large-Scale ML Training
• [EuroSys'23] SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters [also in 2.1]
• [FAST'23] SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training
• [HPCA'23] iCACHE: An Importance-Sampling-Informed Cache for Accelerating I/O-Bound DNN Model Training
• [NeurIPS'22] A Deep Learning Dataloader with Shared Data Preparation
• [CLUSTER'22] Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications
• [ICDE'22] Fluid: Dataset Abstraction and Elastic Acceleration for Cloud-native Deep Learning Training Jobs
• [ATC'21] Refurbish Your Training Data: Reusing Partially Augmented Samples for Faster Deep Neural Network Training
• [FAST'20] Quiver: An Informed Storage Cache for Deep Learning
• [ICPP'20] DIESEL: A Dataset-Based Distributed Storage and Caching System for Large-Scale Deep Learning Training
• [arXiv'19] Faster Neural Network Training with Data Echoing
• [HotCloud'19] The Case for Unifying Data Loading in Machine Learning Clusters

• [SIGMOD'26] Hydraulis: Balancing Large Transformer Model Training via Co-designing Parallel Strategies and Data Assignment
• [EMNLP'25] Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls
• [ICDE'25] Training Data Distribution Estimation for Optimized Pre-Training Data Management
• [arxiv'25] Mixtera: A Data Plane for Foundation Model Training

Data formats

• [ECCV'22] L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
• [VLDB'21] Progressive compressed records: Taking a byte out of deep learning data

Data pipeline fairness and correctness

• [CIDR'21] Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines

Data labeling automation

• [VLDB'18] Snorkel: Rapid Training Data Creation with Weak Supervision

• [ICSE'24] An Empirical Study on Low GPU Utilization of Deep Learning Jobs
• [NSDI'24] Characterization of Large Language Model Development in the Datacenter
• [NSDI'22] MLaaS in the wild: workload analysis and scheduling in large-scale heterogeneous GPU clusters (PAI)
• [ATC'19] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (Philly)

• [arxiv'25] Semantic-Aware Scheduling for GPU Clusters with Large Language Models

• [arxiv'25] Holistic Heterogeneous Scheduling for Autonomous Applications using Fine-grained, Multi-XPU Abstraction

• [arxiv'25] Tesserae: Scalable Placement Policies for Deep Learning Workloads

• [arxiv'25] LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems

• [EuroSys'25] Eva: Cost-Efficient Cloud-Based Cluster Scheduling

• [arxiv'25] TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms

• [arxiv'24] Zeal: Rethinking Large-Scale Resource Allocation with "Decouple and Decompose"

• [TACO'24] Taming Flexible Job Packing in Deep Learning Training Clusters

• [SoCC'24] Kale: Elastic GPU Scheduling for Online DL Model Training

• [arxiv'24] Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling

• [SC'24] PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters

• [OSDI'24] MAST: Global Scheduling of ML Training across Geo-Distributed Datacenters at Hyperscale

• [ASPLOS'24] Heet: Accelerating Elastic Training in Heterogeneous Deep Learning Clusters

• [Middleware'24] Optimal Resource Efficiency with Fairness in Heterogeneous GPU Clusters

• [IPDPS'24] Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster

• [EuroSys'24] Blox: A Modular Toolkit for Deep Learning Schedulers

• [NSDI'24] Swing: Short-cutting Rings for Higher Bandwidth Allreduce

• [NSDI'24] Towards Domain-Specific Network Transport for Distributed DNN Training

• [NSDI'24] Vulcan: Automatic Query Planning for Live ML Analytics

• [NSDI'24] CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

• [Survey 🔍] [ACM CSUR'23] Deep Learning Workload Scheduling in GPU Datacenters: A Survey

• [arxiv'23] Energy-Efficient GPU Clusters Scheduling for Deep Learning

• [SC'23] EasyScale: Accuracy-consistent Elastic Training for Deep Learning

• [ICPP'23] CoTrain: Efficient Scheduling for Large-Model Training upon GPU and CPU in Parallel

• [ICPP'23] Embracing Uncertainty for Equity in Resource Allocation in ML Training

• [SOSP'23] Sia: Heterogeneity-aware, goodput-optimized ML-cluster scheduling

• [NSDI'23] Shockwave: Proactive, Fair, and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

• [EuroSys'23] SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters [also in 1.2]

• [EuroSys'23] Lyra: Elastic Scheduling for Deep Learning Clusters

• [EuroSys'23] ElasticFlow: An Elastic Serverless Training Platform for Distributed Deep Learning

• [ASPLOS'23] Lucid: A Non-intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs

• [arxiv'22] Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads

• [Survey 🔍] [arxiv, 2022] Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

• [SoCC'22] ESCHER: Expressive Scheduling with Ephemeral Resources

• [NSDI'22] MLaaS in the wild: workload analysis and scheduling in large-scale heterogeneous GPU clusters (PAI)

• [OSDI'22] Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters (Synergy)

• [SIGCOMM'22] Multi-resource interleaving for deep learning training (Muri)

• [MLSys'21] Wavelet: Efficient DNN Training with Tick-Tock Scheduling

• [SoCC'21] Chronus: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs

• [SC'21] Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters (Helios)

• [OSDI'21] Privacy Budget Scheduling (DPF)

• [NSDI'21] Elastic Resource Sharing for Distributed Deep Learning (AFS)

• [OSDI'21] Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

• [EuroSys'20] Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning (GandivaFair)

• [NSDI'20] Themis: Fair and Efficient GPU Cluster Scheduling

• [OSDI'20] HiveD: Sharing a GPU Cluster for Deep Learning with Guarantees

• [OSDI'20] Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads (Gavel)

• [EuroSys'20] AlloX: Compute Allocation in Hybrid Clusters

• [MLSys'20] Resource Elasticity in Distributed Deep Learning

• [NSDI'19] Tiresias: A GPU Cluster Manager for Distributed Deep Learning

• [ATC'19] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads (Philly)

• [EuroSys'18] Optimus: an efficient dynamic resource scheduler for deep learning clusters

• [OSDI'18] Gandiva: Introspective Cluster Scheduling for Deep Learning

• [ASPLOS'26] SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips

• [NeurIPS'25] Synergistic Tensor and Pipeline Parallelism

• [arxiv'25] AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training

• [arxiv'25] PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training

• [NeurIPS'25] First Attentions Last: Better Exploiting First Attentions for Efficient Transformer Training

• [arxiv'25] A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training

• [arxiv'25] SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training

• [arxiv'25] AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models

• [arxiv'25] HAPT: Heterogeneity-Aware Automated Parallel Training on Heterogeneous Clusters

• [arxiv'25] Scaling Up Data Parallelism in Decentralized Deep Learning

• [arxiv'25] Zorse: Optimizing LLM Training Efficiency on Heterogeneous GPU Clusters

• [arxiv'25] TrainVerify: Equivalence-Based Verification for Distributed LLM Training

• [arxiv'25] Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage

• [arxiv'25] ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates

• [arxiv'25] Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization

• [arxiv'25] H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips

• [arxiv'25] Balanced and Elastic End-to-end Training of Dynamic LLMs

• [arxiv'25] ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates

• [arxiv'25] SpanTrain: Highly Efficient Cross-domain Model Distributed Training System under Heterogeneous GPUs and Networks in CEE Environment

• [arxiv'25] Parallel Scaling Law for Language Models

• [arxiv'25] Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations

• [arxiv'25] Sailor: Automating Distributed Training over Dynamic, Heterogeneous, and Geo-distributed Clusters

• [arxiv'25] PipeWeaver: Addressing Data Dynamicity in Large Multimodal Model Training with Dynamic Interleaved Pipeline

• [arxiv'25] You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models

• [arxiv'25] WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training

• [arxiv'25] Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training

• [arxiv'25] CFP: Low-overhead Profiling-based Intra-operator Parallelism Generation by Preserving Communication-Free Structures

• [arxiv'25] OrchMLLM: Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training

• [arxiv'25] Cornstarch: Distributed Multimodal Training Must Be Multimodality-Aware

• [arxiv'25] PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization

• [arxiv'25] AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs

• [arxiv'25] Astra: Efficient and Money-saving Automatic Parallel Strategies Search on Heterogeneous GPUs

• [arxiv'25] Scaling Inference-Efficient Language Models

• [arxiv'25] MiniMax-01: Scaling Foundation Models with Lightning Attention

• [SC'25] Hypertron: Efficiently Scaling Large Models by Exploring High-Dimensional Parallelization Space

• [CLUSTER'25] BMPipe: Bubble-Memory Co-Optimization Strategy Planner for Very-Large DNN Training

• [OSDI'25] WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training

• [ISCA'25] FRED: A Wafer-scale Fabric for 3D Parallel DNN Training

• [ISCA'25] MeshSlice: Efficient 2D Tensor Parallelism for Distributed DNN Training

• [ISCA'25] Scaling Llama 3 Training with Efficient Parallelism Strategies

• [ICML'25] HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training

• [MLSys'25] Radius: Range-based Gradient Sparsity for Large Foundation Model Pre-training

• [ICLR'25] TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training

• [INFOCOM'25] Espresso: Cost-Efficient Large Model Training by Exploiting GPU Heterogeneity in the Cloud

• [TPDS'25] HpT: Hybrid Acceleration of Spatio-Temporal Attention Model Training on Heterogeneous Manycore Architectures

• [ASPLOS'25] GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism

• [ASPLOS'25] FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism

• [ASPLOS'25] Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling

• [EuroSys'25] JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs

• [arxiv'24] Automatically Planning Optimal Parallel Strategy for Large Language Models

• [arxiv'24] Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism

• [arxiv'24] Frenzy: A Memory-Aware Serverless LLM Training System for Heterogeneous GPU Clusters

• [arxiv'24] Echo: Simulating Distributed Training At Scale

• [arxiv'24] Scaling Deep Learning Training with MPMD Pipeline Parallelism

• [arxiv'24] Demystifying Workload Imbalances in Large Transformer Model Training over Variable-length Sequences

• [arxiv'24] HETHUB: A Distributed Training System with Heterogeneous Cluster for Large-Scale Models

• [arxiv'24] Data-Centric and Heterogeneity-Adaptive Sequence Parallelism for Efficient LLM Training

• [arxiv'24] Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator

• [arxiv'24] BitPipe: Bidirectional Interleaved Pipeline Parallelism for Accelerating Large Models Training

• [arxiv'24] Cephalo: Harnessing Heterogeneous GPU Clusters for Training Transformer Models

• [arxiv'24] SimpleFSDP: Simpler Fully Sharded Data Parallel with torch.compile

• [arxiv'24] FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression

• [arxiv'24] PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training

• [arxiv'24] Poplar: Efficient Scaling of Distributed DNN Training on Heterogeneous GPU Clusters

• [arxiv'24] DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models

• [arxiv'24] Efficient Multi-Task Large Model Training via Data Heterogeneity-aware Model Management

• [arxiv'24] FlashFlex: Accommodating Large Language Model Training over Heterogeneous Environment

• [arxiv'24] PARALLELGPUOS: A Concurrent OS-level GPU Checkpoint and Restore System using Validated Speculation

• [arxiv'24] Unicron: Economizing Self-Healing LLM Training at Scale

• [arxiv'24] TBA: Faster Large Language Model Training Using SSD-Based Activation Offloading

• [arxiv'24] Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation

• [Survey 🔍] [arxiv'24] Efficient Training of Large Language Models on Distributed Infrastructures: A Survey

• [arxiv'24] LoongTrain: Efficient Training of Long-Sequence LLMs with Head-Context Parallelism

• [arxiv'24] PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning

• [arxiv'24] BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences

• [arxiv'24] Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

• [arxiv'24] Accelerating Heterogeneous Tensor Parallelism via Flexible Workload Control

• [arxiv'24] GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models

• [arxiv'24] BitDelta: Your Fine-Tune May Only Be Worth One Bit

• [arxiv'24] NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models

• [arxiv'24] Accelerating Parallel Sampling of Diffusion Models

• [arxiv'24] Training DNN Models over Heterogeneous Clusters with Optimal Performance

• [arxiv'24] Breaking MLPerf Training: A Case Study on Optimizing BERT

• [arxiv'24] LocMoE: A Low-overhead MoE for Large Language Model Training

• [arxiv'24] Re-evaluating the Memory-balanced Pipeline Parallelism: BPipe

• [arxiv'24] InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding

• [TPDS'24] UMPIPE: Unequal Microbatches-Based Pipeline Parallelism for Deep Neural Network Training

• [Survey 🔍] [ACM CSUR'24] Resource-efficient Algorithms and Systems of Foundation Models: A Survey

• [SOSP'24] Uncovering Nested Data Parallelism and Data Reuse in DNN Computation with FractalTensor

• [SOSP'24] Enabling Parallelism Hot Switching for Efficient Training of Large Language Models

• [TACO'24] ATP: Achieving Throughput Peak for DNN Training via Smart GPU Memory Management

• [NeurIPS'24] Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models

• [NeurIPS'24] SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation

• [SC'24] Accelerating Distributed DLRM Training with Optimized TT Decomposition and Micro-Batching

• [SC'24] Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers

• [SoCC'24] Distributed training of large language models on AWS Trainium

• [TPDS'24] AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth Cost

• [SOSP'24] Enabling Parallelism Hot Switching for Efficient Training of Large Language Models

• [SOSP'24] TENPLEX: Changing Resources of Deep Learning Jobs using Parallelizable Tensor Collections

• [ICPP'24] AutoPipe: Automatic Configuration of Pipeline Parallelism in Shared GPU Cluster

• [COLM'24] LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers

• [OSDI'24] nnScaler: Constraint-Guided Parallelization Plan Generation for Deep Learning Training

  • [arxiv'23] SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

• [ATC'24] Accelerating the Training of Large Language Models using Efficient Activation Rematerialization and Optimal Hybrid Parallelism

• [ATC'24] Metis: Fast Automatic Distributed Training on Heterogeneous GPUs

• [ATC'24] FwdLLM: Efficient Federated Finetuning of Large Language Models with Perturbed Inferences

• [ATC'24] OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model

• [HPDC'24] DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models

• [ICML'24] Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training

• [ICML'24] Integrated Hardware Architecture and Device Placement Search

• [MLSys'24] DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines

• [MLSys'24] Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping

• [MobiCom'24] Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices

• [EuroSys'24] DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

• [EuroSys'24] ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling

• [EuroMLSys@EuroSys'24] ML Training with Cloud GPU Shortages: Is Cross-Region the Answer?

• [ASPLOS'24] AdaPipe: Optimizing Pipeline Parallelism with Adaptive Recomputation and Partitioning

• [ASPLOS'24] PrimePar: Efficient Spatial-temporal Tensor Partitioning for Large Transformer Model Training

• [EuroSys'24] Aceso: Efficient Parallel DNN Training through Iterative Bottleneck Alleviation

• [NSDI'24] MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs

• [NSDI'24] DISTMM: Accelerating Distributed Multi-modal Model Training

• [NSDI'24] Accelerating Neural Recommendation Training with Embedding Scheduling

• [NSDI'24] Resiliency at Scale: Managing Google’s TPUv4 Machine Learning Supercomputer

• [NSDI'24] QuickUpdate: a Real-Time Personalization System for Large-Scale Recommendation Models

• [NSDI'24] Scaling Large Language Model Training to More Than 10,000 GPUs

• [TKDE'24] Improving Automatic Parallel Training via Balanced Memory Workload Optimization

  • extended version of Galvatron (VLDB'23)
  • arxiv version (2023): link

• [ICLR'24] Zero Bubble (Almost) Pipeline Parallelism

• [ICLR'24] CO2: Efficient Distributed Training with Full Communication-Computation Overlap

  • arxiv

• [AAMAS'24] Holonic Learning: A Flexible Agent-based Distributed Machine Learning Framework

• [VLDB'24] Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads

• [HPCA'24] Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search

• [NSDI'24] Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances

• [EuroSys'24] HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis

• [arxiv'23] vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training

• [arxiv'23] ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU

• [arxiv'23] FlexModel: A Framework for Interpretability of Distributed Large Language Models

• [arxiv'23] Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment

• [arxiv'23] RTP: Rethinking Tensor Parallelism with Memory Deduplication

• [arxiv'23] FP8-LM: Training FP8 Large Language Models

• [arxiv'23] Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

• [arxiv'23] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

• [arxiv'23] A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

• [arxiv'23] FLM-101B: An Open LLM and How to Train It with $100K Budget

• [arxiv'23] UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming

• [arxiv'23] Modeling Parallel Programs using Large Language Models

• [arxiv'23] Proteus: Simulating the Performance of Distributed DNN Training

• [arxiv'23] Automated Tensor Model Parallelism with Overlapped Communication for Efficient Foundation Model Training

• [arxiv'23] Decoupled Model Schedule for Deep Learning Training

• [arxiv'23] RAF: Holistic Compilation for Deep Learning Model Training

• [arxiv'23] Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches

• [arxiv'23] Does compressing activations help model parallel training?

• [arxiv'23] Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models

• [arxiv'23] Scaling Vision Transformers to 22 Billion Parameters

• [arxiv'23] Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

• [arxiv'23] TAP: Accelerating Large-Scale DNN Training Through Tensor Automatic Parallelisation

• [arxiv'23] SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

• [arxiv'23] ATP: Adaptive Tensor Parallelism for Foundation Models

• [ICPP'23] Mercury: Fast and Optimal Device Placement for Large Deep Learning Models

• [IPDPS'23] MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism

• [CLUSTER'23] Prophet: Fine-grained Load Balancing for Parallel Training of Large-scale MoE Models

• [NeurIPS'23] ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks

• [NeurIPS'23] DeepPCR: Parallelizing Sequential Operations in Neural Networks

• [DAC'23] MixPipe: Efficient Bidirectional Pipeline Parallelism for Training Large-Scale Models

• [SC'23] Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency

• [SOSP'23] PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

• [SOSP'23] Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates

• [TPDS'23] Fold3D: Rethinking and Parallelizing Computational and Communicational Tasks in the Training of Large DNN Models

• [MICRO'23] Grape: Practical and Efficient Graphed Execution for Dynamic Deep Neural Networks on GPUs

• [HPCA'23] Phloem: Automatic Acceleration of Irregular Applications with Fine-Grain Pipeline Parallelism

• [ACL'23] Sequence Parallelism: Long Sequence Training from System Perspective

• [CCGrid'23] A Deep Learning Pipeline Parallel Optimization Method

• [OSDI'23] MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms

• [ATC'23] Accelerating Distributed MoE Training and Inference with Lina

• [ATC'23] SmartMoE: Efficiently Training Sparsely-Activated Models through Combining Offline and Online Parallelization

• [ATC'23] MSRL: Distributed Reinforcement Learning with Dataflow Fragments

• [Survey 🔍] [TPDS'23] A Survey on Auto-Parallelism of Large-Scale Deep Learning Training

• [ICML'23] SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

• [ICML'23] BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models

• [ICS'23] A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training

• [NSDI'23] TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs

• [NSDI'23] Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs

• [NSDI'23] ARK: GPU-driven Code Execution for Distributed Deep Learning

• [SIGMOD'23] FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

• [MLSys'23] On Optimizing the Communication of Model Parallelism

• [MLSys'23] MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

• [MLSys'23] Tutel: Adaptive Mixture-of-Experts at Scale

• [TPDS'23] Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models

• [PPoPP'23] Elastic Averaging for Efficient Pipelined DNN Training

• [PPoPP'23] Efficient All-Reduce for Distributed DNN Training in Optical Interconnect Systems

• [VLDB'23] MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

• [VLDB'23] Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

• [ASPLOS'23] Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers

• [ASPLOS'23] Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression

• [arxiv'22] Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

• [arxiv'22] Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

• [ICPP'22] Tesseract: Parallelize the Tensor Parallelism Efficiently

• [MLSys'22] Synthesizing optimal parallelism placement and reduction strategies on hierarchical systems for deep learning

  • arxiv

• [NeurIPS'22] Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees

• [SoCC'22] Accelerating Large-Scale Distributed Neural Network Training with SPMD Parallelism

• [MLSys'22] Pathways: Asynchronous distributed dataflow for ML

• [MLSys'22] SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud

• [MLSys'22] Efficient Strong Scaling Through Burst Parallel Training

• [EuroSys'22] Varuna: scalable, low-cost training of massive deep learning models

• [ATC'22] Whale: Efficient Giant Model Training over Heterogeneous GPUs

• [NeurIPS'22] AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness

• [PPoPP'22] FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models

• [ICML'22] DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

• [ICML'22] GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

• [HPDC'22] Hare: Exploiting Inter-job and Intra-job Parallelism of Distributed Machine Learning on Heterogeneous GPUs

• [OSDI'22] Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning

• [NSDI'22] Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

• [arxiv'21] Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training

• [arxiv'21] GSPMD: General and Scalable Parallelization for ML Computation Graphs

• [JMLR'21] Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

• [TPDS'21] TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism

• [ATC'21] Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism

• [SIGMOD'21] Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce [also in 2.10]

• [MLSys'21] PipeMare: Asynchronous Pipeline Parallel DNN Training

• [ICLR'21] GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

• [NeurIPS'21] Piper: Multidimensional Planner for DNN Parallelization

• [ICML'21] Memory-Efficient Pipeline-Parallel DNN Training

• [ICML'21] TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models

• [ICML'21] PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

• [SC'21] Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines

• [SC'21] Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM (PTD-P or Megatron-LM v2)

• [FAST'21] Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs

• [PPoPP'21] DAPPLE: a pipelined data parallel approach for training large models

• [VLDB'21] Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches

• [HPCA'20] AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators

• [NeurIPS'20] Efficient Algorithms for Device Placement of DNN Graph Operators

• [arxiv'20] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

• [KDD'20 Tutorial] DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters

• [VLDB'20] PyTorch Distributed: Experiences on Accelerating Data Parallel Training

• [OSDI'20] A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters (BytePS)

• [SOSP'19] PipeDream: Generalized Pipeline Parallelism for DNN Training

• [NeurIPS'20] Language Models are Few-Shot Learners [From OpenAI]

  • arxiv

• [arxiv'20] Scaling Laws for Neural Language Models [From OpenAI]

• [HPCA'19] HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array

• [IEEE MICRO'19] Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

• [MLSys'19] Beyond data and model parallelism for deep neural networks (FlexFlow)

• [MLSys'19] TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

• [EuroSys'19] Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks

• [EuroSys'19] Supporting Very Large Models using Automatic Dataflow Graph Partitioning (Tofu)

• [SOSP'19] A Generic Communication Scheduler for Distributed DNN Training Acceleration

• [NeurIPS'19] Mesh-TensorFlow: Deep Learning for Supercomputers

• [NeurIPS'19] GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

• [ICML'18] Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

• [Survey 🔍] [IJCAI'22] Survey on Effcient Training of Large Neural Networks

• [Survey 🔍] [ACM CSUR'19] Demystifying Parallel and Distributed Deep Learning

• [Survey 🔍] [ACM CSUR'19] Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools

• [OSDI'23] Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters
• [NSDI'23] ModelKeeper: Accelerating DNN Training via Automated Training Warmup
• [OSDI'20] Retiarii: A Deep Learning Exploratory-Training Framework

For comprehensive list of GNN systems papers, refer to https://github.com/chwan1016/awesome-gnn-systems.

• [SC'25] Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training
• [SIGMOD'25] NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters
• [ICDE'25] CaliEX: A Disk-Based Large-Scale GNN Training System with Joint Design of Caching and Execution
• [arxiv'25] Plexus: Taming Billion-edge Graphs with 3D Parallel GNN Training
• [HPCA'25] Mithril: A Scalable System for Deep GNN Training
• [arxiv'25] Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
• [VLDB'25] NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism
• [arxiv'24] FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
• [ICPP'24] GNNDrive: Reducing Memory Contention and I/O Congestion for Disk-based GNN Training
• [VLDB'24] NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
• [arxiv'23] ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
• [arxiv'23] Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance
• [arxiv'23] GNNPipe: Accelerating Distributed Full-Graph GNN Training with Pipelined Model Parallelism
• [MLSys'23] Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
• [SIGMOD'23] DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU
• [OSDI'23] MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU Platforms
• [EuroSys'23] MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
• [KDD'22] Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs
• [VLDB'22] TGL: a general framework for temporal GNN training on billion-scale graphs
• [OSDI'21] P3: Distributed Deep Graph Learning at Scale

• [AAAI'26] Lethe: Layer- and Time-Adaptive KV Cache Pruning for Reasoning-Intensive LLM Serving

• [EuroSys'26] FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters

• [EuroSys'26] KunServe: Parameter-centric Memory Management for Efficient Memory Overloading Handling in LLM Serving

• [EuroSys'26] TokenFlow: Responsive LLM Text Streaming Serving under Request Burst via Preemptive Scheduling

• [arxiv'25] TokenScale: Timely and Accurate Autoscaling for Disaggregated LLM Serving with Token Velocity

• [arxiv'25] AugServe: Adaptive Request Scheduling for Augmented Large Language Model Inference Serving

• [arxiv'25] Accelerating Large-Scale Reasoning Model Inference with Sparse Self-Speculative Decoding

• [arxiv'25] SIMPLE: Disaggregating Sampling from GPU Inference into a Decision Plane for Faster Distributed LLM Serving

• [arxiv'25] OmniInfer: System-Wide Acceleration Techniques for Optimizing LLM Serving Throughput and Latency

• [arxiv'25] OOCO: Latency-disaggregated Architecture for Online-Offline Co-locate LLM Serving

• [arxiv'25] Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems

• [arxiv'25] Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms

• [arxiv'25] CLO: Efficient LLM Inference System with CPU-Light KVCache Offloading via Algorithm-System Co-Design

• [arxiv'25] FengHuang: Next-Generation Memory Orchestration for AI Inferencing

• [arxiv'25] FLUXSERVE: Serving a Variety of LLMs for Best-Effort Efficiency via Dynamic Temperature-Aware Multiplexing

• [arxiv'25] Synera: Synergistic LLM Serving across Device and Cloud at Scale

• [arxiv'25] DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing

• [Middleware'25] Argus: Quality-Aware High-Throughput Text-to-Image Inference Serving System

• [arxiv'25] From Models to Operators: Rethinking Autoscaling Granularity for Large Generative Models

• [arxiv'25] TapOut: A Bandit-Based Approach to Dynamic Speculative Decoding

• [NeurIPS'25] SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications

• [arxiv'25] FREESH: Fair, Resource- and Energy-Efficient Scheduling for LLM Serving on Heterogeneous GPUs

• [EMNLP'25] Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication

• [arxiv'25] Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs

• [MICRO'25] MX+: Pushing the Limits of Microscaling Formats for Efficient Large Language Model Serving

• [MICRO'25] Kelle: Co-design KV Caching and eDRAM for Efficient LLM Serving in Edge Computing

• [arxiv'25] SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference

• [arxiv'25] From Tokens to Layers: Redefining Stall-Free Scheduling for LLM Serving with Layered Prefill

• [CLUSTER'25] Scalable and Fast Inference Serving via Hybrid Communication Scheduling on Heterogeneous Networks

• [CLUSTER'25] Rock: Serving Multimodal Models in Cloud with Heterogeneous-Aware Resource Orchestration for Thousands of LoRA Adapters

• [arxiv'25] TridentServe: A Stage-level Serving System for Diffusion Pipelines

• [arxiv'25] MACE: A Hybrid LLM Serving System with Colocated SLO-aware Continuous Retraining Alignment

• [Survey 🔍] [ACM CSUR'25] Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems

• [SOSP'25] Aegaeon: Effective GPU Pooling for Concurrent LLM Serving on the Market

• [SOSP'25] IC-Cache: Efficient Large Language Model Serving via In-context Caching

• [SOSP'25] DiffKV: Differentiated Memory Management for Large Language Models with Parallel KV Compaction

• [arxiv'25] TetriServe: Efficient DiT Serving for Heterogeneous Image Generation

• [arxiv'25] Parallax: Efficient LLM Inference Service over Decentralized Environment

• [arxiv'25] RServe: Overlapping Encoding and Prefill for Efficient LMM Inference

• [arxiv'25] Cronus: Efficient LLM inference on Heterogeneous GPU Clusters via Partially Disaggregated Prefill

• [arxiv'25] Shift Parallelism: Low-Latency, High-Throughput LLM Inference for Dynamic Workloads

• [COLM'25] OverFill: Two-Stage Models for Efficient Language Model Decoding

• [ACM MM'25] TinyServe: Query-Aware Cache Selection for Efficient LLM Serving

• [arxiv'25] Scaling Up Throughput-oriented LLM Inference Applications on Heterogeneous Opportunistic GPU Clusters with Pervasive Context Management

• [SC'25] Hetis: Serving LLMs in Heterogeneous GPU Clusters with Fine-grained and Dynamic Parallelism

• [arxiv'25] FineServe: Precision-Aware KV Slab and Two-Level Scheduling for Heterogeneous Precision LLM Serving

• [arxiv'25] AdaptCache: KV Cache Native Storage Hierarchy for Low-Delay and High-Quality Language Model Serving

• [arxiv'25] Predictable LLM Serving on GPU Clusters

• [SIGCOMM'25] SCX: Stateless KV-Cache Encoding for Cloud-Scale Confidential Transformer Serving

• [arxiv'25] Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference

• [arxiv'25] Rethinking Caching for LLM Serving Systems: Beyond Traditional Heuristics

• [OSDI'25] BlitzScale: Fast and Live Large Model Autoscaling with O(1) Host Caching

• [OSDI'25] WaferLLM: Large Language Model Inference at Wafer Scale

• [OSDI'25] NanoFlow: Towards Optimal Large Language Model Serving Throughput

• [arxiv'25] TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving

• [arxiv'25] HyperFlexis: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling

• [arxiv'25] Equinox: Holistic Fair Scheduling in Serving Large Language Models

• [arxiv'25] Efficient Mixed-Precision Large Language Model Inference with TurboMind

• [ICML'25] Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving

• [arxiv'25] Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud

• [arxiv'25] Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

• [arxiv'25] Prefill-Decode Aggregation or Disaggregation? Unifying Both for Goodput-Optimized LLM Serving

• [arxiv'25] Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling

• [ACL'25] MiniKV: Pushing the Limits of 2-Bit KV Cache via Compression and System Co-Design for Efficient Long Context Inference

• [ACL'25] StitchLLM: Serving LLMs, One Block at a Time

• [ACL'25] SPECTRA: Faster Large Language Model Inference with Optimized Internal and External Speculation

• [arxiv'25] Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding

• [arxiv'25] Proactive Intra-GPU Disaggregation of Prefill and Decode in LLM Serving

• [arxiv'25] MIRAGE: KV Cache Optimization through Parameter Remapping for Multi-tenant LLM Serving

• [CODEML @ ICML'25] TorchAO: PyTorch-Native Training-to-Serving Model Optimization

• [arxiv'25] On Evaluating Performance of LLM Inference Serving Systems

• [arxiv'25] PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications

• [ICML'25] EPIC: Efficient Position-Independent Caching for Serving Large Language Models

• [arxiv'25] SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference

• [arxiv'25] Utility-Driven Speculative Decoding for Mixture-of-Experts

• [ATC'25] DEEPSERVE: Serverless Large Language Model Serving at Scale

• [ISCA'25] WindServe: Efficient Phase-Disaggregated LLM Serving with Stream-based Dynamic Scheduling

• [ISCA'25] Hybe: GPU-NPU Hybrid System for Efficient LLM Inference with Million-Token Context Window

• [ICLR'25] TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention

• [arxiv'25] Cascadia: A Cascade Serving System for Large Language Models

• [arxiv'25] Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing

• [arxiv'25] SkyLB: A Locality-Aware Cross-Region Load Balancer for LLM Inference

• [arxiv'25] EmbAdvisor: Adaptive Cache Management for Sustainable LLM Serving

• [arxiv'25] SCORPIO: Serving the Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference

• [arxiv'25] HybridServe: Efficient Serving of Large AI Models with Confidence-Based Cascade Routing

• [arxiv'25] ServerlessLoRA: Minimizing Latency and Cost in Serverless Inference for LoRA-Based LLMs

• [arxiv'25] TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference

• [arxiv'25] Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving

• [OSDI'25] Clover: Exploiting Intra-device Parallelism for High Throughput Large Language Model Serving

• [arxiv'25] ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production

• [arxiv'25] ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor

• [arxiv'25] Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management

• [arxiv'25] Prism: Unleashing GPU Sharing for Cost-Efficient Multi-LLM Serving

• [arxiv'25] Tempo: Application-aware LLM Serving with Mixed SLO Requirements

• [arxiv'25] Ascendra: Dynamic Request Prioritization for Efficient LLM Serving

• [arxiv'25] Medha: Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations

• [arxiv'25] Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving

• [arxiv'25] Bullet: Boosting GPU Utilization for LLM Serving via Dynamic Spatial-Temporal Orchestration

• [Survey 🔍] [arxiv'25] Taming the Titans: A Survey of Efficient LLM Inference Serving

• [MLSys'25] SOLA: Optimizing SLO Attainment for Large Language Model Serving with State-Aware Scheduling

• [MLSys'25] Marconi: Prefix Caching for the Era of Hybrid LLMs

• [arxiv'25] PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation

• [arxiv'25] Circinus: Efficient Query Planner for Compound ML Serving

• [arxiv'25] HPU: High-Bandwidth Processing Unit for Scalable, Cost-effective LLM Inference via GPU Co-processing

• [Mobicom'25] D2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving

• [arxiv'25] SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference

• [arxiv'25] gLLM: Global Balanced Pipeline Parallelism System for Distributed LLM Serving with Token Throttling

• [arxiv'25] Optimizing SLO-oriented LLM Serving with PD-Multiplexing

• [arxiv'25] SLO-Aware Scheduling for Large Language Model Inferences

• [arxiv'25] Cost-Efficient LLM Serving in the Cloud: VM Selection with KV Cache Offloading

• [ISPASS'25] Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures

• [arxiv'25] HELIOS: Adaptive Model And Early-Exit Selection for Efficient LLM Inference Serving

• [arxiv'25] DynaServe: Unified and Elastic Tandem-Style Execution for Dynamic Disaggregated LLM Serving

• [arxiv'25] Efficient LLM Serving on Hybrid Real-time and Best-effort Requests

• [arxiv'25] SLOs-Serve: Optimized Serving of Multi-SLO LLMs

• [arxiv'25] Understanding and Optimizing Multi-Stage AI Inference Pipelines

• [arxiv'24] Fast and Live Model Auto Scaling with O(1) Host Caching

• [SIGMOD'25] Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving

• [EuroMLSys'25] Performance Aware LLM Load Balancer for Mixed Workloads

• [MLSys'25] Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving

• [arxiv'25] WaferLLM: A Wafer-Scale LLM Inference System

• [HPCA'25] PAISE: PIM-Accelerated Inference Scheduling Engine for Transformer-based LLM

• [HPCA'25] throttLL'eM: Predictive GPU Throttling for Energy Efficient LLM Inference Serving

• [arxiv'25] Niyama : Breaking the Silos of LLM Inference Serving

• [ASPLOS'25] Aqua: Network-Accelerated Memory Offloading for LLMs in Scale-Up GPU Domains

• [ASPLOS'25] Past-Future Scheduler for LLM Serving under SLA Guarantees

• [ASPLOS'25] Accelerating LLM Serving for Multi-turn Dialogues with Efficient Resource Management

• [EuroSys'25] SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs

• [EuroSys'25] Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters

• [arxiv'25] Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation

• [EuroSys'25] NeuStream: Bridging Deep Learning Serving and Stream Processing

• [arxiv'25] ModServe: Scalable and Resource-Efficient Large Multimodal Model Serving

• [arxiv'25] PipeBoost: Resilient Pipelined Architecture for Fast Serverless LLM Scaling

• [ISCA'25] Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization

• [arxiv'25] Jenga: Effective Memory Management for Serving LLM with Heterogeneity

• [arxiv'25] AccelGen: Heterogeneous SLO-Guaranteed High-Throughput LLM Inference Serving for Diverse Applications

• [FAST'25] Mooncake: Trading More Storage for Less Computation — A KVCache-centric Architecture for Serving LLM Chatbot

• [arxiv'25] Collaborative Speculative Inference for Efficient LLM Inference Serving

• [NSDI'25] SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads

• [arxiv'25] Seesaw: High-throughput LLM Inference via Model Re-sharding

• [arxiv'25] SpecServe: Efficient and SLO-Aware Large Language Model Serving with Adaptive Speculative Decoding

• [arxiv'25] ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput

• [arxiv'25] Long-Context Inference with Retrieval-Augmented Speculative Decoding

• [WWW'25] External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

• [arxiv'25] Make LLM Inference Affordable to Everyone: Augmenting GPU Memory with NDP-DIMM

• [arxiv'25] KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse

• [arxiv'25] Serving Models, Fast and Slow:Optimizing Heterogeneous LLM Inferencing Workloads at Scale

• [arxiv'25] LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention

• [arxiv'25] HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading

• [arxiv'25] Autellix: An Efficient Serving Engine for LLM Agents as General Programs

• [MLSys'25] ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments

• [ICLR'25] HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment

• [arxiv'25] Memory Offloading for Large Language Model Inference with Latency SLO Guarantees

• [EuroSys'25] SkyServe: Serving AI Models across Regions and Clouds with Spot Instances

• [ASPLOS'25] Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow

• [ASPLOS'25] Dilu: Enabling GPU Resourcing-on-Demand for Serverless DL Serving via Introspective Elasticity

• [arxiv'25] MPIC: Position-Independent Multimodal Context Caching System for Efficient MLLM Serving

• [arxiv'25] Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

• [arxiv'25] Towards Efficient Large Multimodal Model Serving

• [arxiv'25] HeteroLLM: Accelerating Large Language Model Inference on Mobile SoCs platform with Heterogeneous AI Accelerators

• [arxiv'25] HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location

• [arxiv'25] Locality-aware Fair Scheduling in LLM Serving

• [arxiv'25] DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs

• [arxiv'25] DeepFlow: Serverless Large Language Model Serving at Scale

• [arxiv'25] iServe: An Intent-based Serving System for LLMs

• [arxiv'25] AdaServe: SLO-Customized LLM Serving with Fine-Grained Speculative Decoding

• [arxiv'25] EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation

• [arxiv'25] OMEGA: A Low-Latency GNN Serving System for Large Graphs

• [arxiv'25] PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM Serving

• [arxiv'25] Hierarchical Autoscaling for Large Language Model Serving with Chiron

• [arxiv'25] Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management

• [arxiv'25] Accelerated Diffusion Models via Speculative Sampling

• [MLSys'25] FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving

• [EuroSys'25] A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro

• [arxiv'24] LLM Inference Unveiled: Survey and Roofline Model Insights

• [arxiv'24] Efficiently Serving LLM Reasoning Programs with Certaindex

• [arxiv'24] LoL-PIM: Long-Context LLM Decoding with Scalable DRAM-PIM System

• [arxiv'24] TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic Applications

• [arxiv'24] Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference

• [arxiv'24] KunServe: Elastic and Efficient Large Language Model Serving with Parameter-centric Memory Management

• [arxiv'24] Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels

• [arxiv'24] SYMPHONY: Improving Memory Management for LLM Inference Workloads

• [arxiv'24] A System for Microserving of LLMs

• [arxiv'24] HashAttention: Semantic Sparsity for Faster Inference

• [arxiv'24] SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices

• [arxiv'24] Unifying KV Cache Compression for Large Language Models with LeanKV

• [arxiv'24] PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers

• [Survey 🔍] [ACM CSUR'24] Resource-efficient Algorithms and Systems of Foundation Models: A Survey

• [arxiv'24] BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

• [ICML'25] SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization [Code]

• [ICLR'25] SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration [Code]

• [ICML'25] SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference [Code]

• [arxiv'24] Optimizing Speculative Decoding for Serving Large Language Models Using Goodput

• [ACL'24] LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

• [ACL'24] SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

• [arxiv'24] EcoServe: Maximizing Multi-Resource Utilization with SLO Guarantees in LLM Serving

• [IPDPS'24] Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference

• [arxiv'24] EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference

• [NeurIPS'24] Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting

• [NeurIPS'24] Toward Efficient Inference for Mixture of Experts

• [NeurIPS'24] Sequoia: Scalable and Robust Speculative Decoding

• [arxiv'24] Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection

• [SC'24] PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation

• [SC'24] SMIless: Serving DAG-based Inference with Dynamic Invocations under Serverless Computing

• [arxiv'24] SuffixDecoding: A Model-Free Approach to Speeding Up Large Language Model Inference

• [arxiv'24] V-LoRA: An Efficient and Flexible System Boosts Vision Applications with LoRA LMM

• [SenSys'24] LiteMoE: Customizing On-device LLM Serving via Proxy Submodel Tuning

• [arxiv'24] HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference

• [arxiv'24] NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference

• [MICRO'24] Pushing the Performance Envelope of DNN-based Recommendation Systems Inference on GPUs

• [arxiv'24] VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration

• [arxiv'24] ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference

• [arxiv'24] Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs

• [arxiv'24] POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference

• [PML4LRS @ ICLR2024] Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

• [arxiv'24] MagicPIG: LSH Sampling for Efficient LLM Generation

• [arxiv'24] Revisiting SLO and Goodput Metrics in LLM Serving

• [arxiv'24] EPIC: Efficient Position-Independent Context Caching for Serving Large Language Models

• [arxiv'24] ParallelSpec: Parallel Drafter for Efficient Speculative Decoding

• [EuroSys'25] Fast State Restoration in LLM Serving with HCache

• [arxiv'24] SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation

• [arxiv'24] vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

• [arxiv'24] Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations

• [arxiv'24] CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts

• [arxiv'24] DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency

• [HPCA'24] KRISP: Enabling Kernel-wise RIght-sizing for Spatial Partitioned GPU Inference Servers

• [arxiv'24] Missile: Fine-Grained, Hardware-Level GPU Resource Isolation for Multi-Tenant DNN Inference

• [NeurIPS'24] Efficient LLM Scheduling by Learning to Rank

• [arxiv'24] P/D-Serve: Serving Disaggregated Large Language Model at Scale

• [arxiv'24] MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models

• [SOSP'24] PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

• [SOSP'24] LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism

• [SOSP'24] Improving DNN Inference Throughput Using Practical, Per-Input Compute Adaptation

• [SOSP'24] Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving

• [arxiv'24] LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale

• [ICPP'24] GMM: An Efficient GPU Memory Management-based Model Serving System for Multiple DNN Inference Models

• [SIGCOMM'24] CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

• [ES-FoMO @ ICML'24] CO2: Precise Attention Score Observation for improving KV Cache Replacement in Large Language Models

• [OSDI'24] dLoRA: Dynamically Orchestrating Requests and Adapters for LoRA LLM Serving

• [OSDI'24] Parrot: Efficient Serving of LLM-based Applications with Semantic Variable

• [OSDI'24] USHER: Holistic Interference Avoidance for Resource Optimized ML Inference

• [OSDI'24] Fairness in Serving Large Language Models

• [OSDI'24] MonoNN: Enabling a New Monolithic Optimization Space for Neural Network Inference Tasks on Modern GPU-Centric Architectures

• [OSDI'24] Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve

• [OSDI'24] ServerlessLLM: Low-Latency Serverless Inference for Large Language Models

• [OSDI'24] InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management

• [OSDI'24] Llumnix: Dynamic Scheduling for Large Language Model Serving

• [OSDI'24] DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

• [ATC'24] Power-aware Deep Learning Model Serving with μ-Serve

• [ATC'24] Fast Inference for Probabilistic Graphical Models

• [ATC'24] Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention

• [ATC'24] PUZZLE: Efficiently Aligning Large Language Models through Light-Weight Context Switch

• [ATC'24] Quant-LLM: Accelerating the Serving of Large Language Models via FP6-Centric Algorithm-System Co-Design on Modern GPUs

• [TPDS'24] ElasticBatch: A Learning-Augmented Elastic Scheduling System for Batch Inference on MIG

• [Survey 🔍] [arxiv'24] LLM Inference Serving: Survey of Recent Advances and Opportunities

• [arxiv'24] Metron: Holistic Performance Evaluation Framework for LLM Inference Systems

• [arxiv'24] Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead

• [arxiv'24] One Queue Is All You Need: Resolving Head-of-Line Blocking in Large Language Model Serving

• [OSDI'24] Parrot: Efficient Serving of LLM-based Applications with Semantic Variable

• [arxiv'24] MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool

• [ISCA'24] ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models

• [ISCA'24] Splitwise: Efficient generative LLM inference using phase splitting

• [ICML'24] Break the Sequential Dependency of LLM Inference Using Lookahead Decoding

• [ICML'24] Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

• [ICML'24] HexGen: Generative Inference of Large Language Model over Heterogeneous Environment

• [ICML'24] EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

• [ICML'24] MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving

• [HPCA'24] An LPDDR-based CXL-PNM Platform for TCO-efficient Inference of Transformer-based Large Language Models

• [MobiSys'24] ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling

• [MobiSys'24] Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs

• [arxiv'24] Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference

• [arxiv'24] HawkVision: Low-Latency Modeless Edge AI Serving

• [MLSys'24] HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

• [MLSys'24] S-LoRA: Serving Thousands of Concurrent LoRA Adapters

• [MLSys'24] Vidur: A Large-Scale Simulation Framework For LLM Inference

• [arxiv'24] The CAP Principle for LLM Serving

• [WWW'24] λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing

• [ICML'24] CLLMs: Consistency Large Language Models

• [arxiv'24] BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models

• [EuroSys'24] Model Selection for Latency-Critical Inference Serving

• [arxiv'24] Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity

• [arxiv'24] Learn To be Efficient: Build Structured Sparsity in Large Language Models

• [arxiv'24] Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling

• [ISCA'24] Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

• [arxiv'24] Minions: Accelerating Large Language Model Inference with Adaptive and Collective Speculative Decoding

• [arxiv'24] ALTO: An Efficient Network Orchestrator for Compound AI Systems

• [ASPLOS'24] ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference

• [ASPLOS'24] NeuPIMs: NPU-PIM Heterogeneous Acceleration for Batched LLM Inferencing

• [arxiv'24] ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys

• [arxiv'24] Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve

• [ICML'24] DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving

• [ICLR'24] Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

• [arxiv'24] FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient Finetuning

• [arxiv'24] Wisdom of Committee: Distilling from Foundation Model to SpecializedApplication Model

• [arxiv'24] RelayAttention for Efficient Large Language Model Serving with Long System Prompts

• [arxiv'24] LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization

  • [PPoPP'24 poster] POSTER: LLM-PQ:Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization

• [NSDI'24] Approximate Caching for Efficiently Serving Diffusion Models

• [arxiv'24] APIServe: Efficient API Support for Large-Language Model Inferencing

• [arxiv'24] ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models

• [arxiv'24] MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

• [arxiv'24] FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design

• [arxiv'24] Accelerating Retrieval-Augmented Language Model Serving with Speculation

• [arxiv'24] CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference

• [arxiv'24] Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads

• [arxiv'24] DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

• [Survey 🔍] [arxiv'24] Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding

• [arxiv'24] Learned Best-Effort LLM Serving

• [arxiv'24] Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache

• [VLDB'24] Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity

• [ASPLOS'24] SpotServe: Serving Generative Large Language Models on Preemptible Instances

• [ASPLOS'24] SpecInfer: Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree Verification

• [arxiv'23] DeltaZip: Multi-Tenant Language Model Serving via Delta Compression

• [EMNLP'23] Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding

• [arxiv'23] Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding

• [arxiv'23] Fairness in Serving Large Language Models

• [arxiv'23] Moirai: Towards Optimal Placement for Distributed Inference on Heterogeneous Devices

• [arxiv'23] Punica: Multi-Tenant LoRA Serving

• [arxiv'23] Pipeline Parallelism for DNN Inference with Practical Performance Guarantees

• [arxiv'23] SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills

• [arxiv'23] High-throughput Generative Inference of Large Language Models with a Single GPU

• [NeurIPS'23] SpecTr: Fast Speculative Decoding via Optimal Transport

• [HPDC'23] Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources

• [SOSP'23] Paella: Low-latency Model Serving with Virtualized GPU Scheduling

• [SOSP'23] Efficient Memory Management for Large Language Model Serving with PagedAttention

• [MLSys'23] Efficiently Scaling Transformer Inference

• [EuroSys'23] Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access

• [EuroSys'23] Tabi: An Efficient Multi-Level Inference System for Large Language Models

• [EuroSys'23] Pocket: ML Serving from the Edge

• [OSDI'23] AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving

• [NSDI'23] SHEPHERD: Serving DNNs in the Wild

• [VLDB'23] Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures

• [ICML'23] Fast Inference from Transformers via Speculative Decoding

• [SIGMOD'22] Serverless Data Science - Are We There Yet? A Case Study of Model Serving

• [OSDI'22] Orca: A Distributed Serving System for Transformer-Based Generative Models

• [OSDI'22] Microsecond-scale Preemption for Concurrent GPU-accelerated DNN Inferences

• [ATC'22] SOTER: Guarding Black-box Inference for General Neural Networks at the Edge

• [ATC'22] Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing

• [ATC'22] Tetris: Memory-efficient Serverless Inference through Tensor Sharing

• [ATC'22] PetS: A Unified Framework for Parameter-Efficient Transformers Serving

• [ATC'21] INFaaS: Automated Model-less Inference Serving

• [SoCC'21] Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving

• [arxiv'21] Supporting Massive DLRM Inference through Software Defined Memory

• [MobiCom'20] SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

• [SC'25] UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-Tiling
• [SC'25] RingX: Scalable Parallel Attention for Long-Context Learning on HPC
• [NeurIPS'25 Spotlight] SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training [Code]
• [arxiv'25] SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention [Code]
• [MLSys'25] FastTree: Optimizing Attention Kernel and Runtime for Tree-Structured LLM Inference
• [MLSys'25] FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
• [NeurIPS'24] FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
• [ICLR'24] FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• [NeurIPS'22] FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

• [EuroSys'26] Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading

• [EuroSys'26] MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production

• [arxiv'25] Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems

• [arxiv'25] MicroMoE: Fine-Grained Load Balancing for Mixture-of-Experts with Token Scheduling

• [arxiv'25] MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping

• [arxiv'25] MoE-SpeQ: Speculative Quantized Decoding with Proactive Expert Prefetching and Offloading for Mixture-of-Experts

• [arxiv'25] Pre-Attention Expert Prediction and Prefetching for Mixture-of-Experts Large Language Models

• [arxiv'25] FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models

• [arxiv'25] DualSparse-MoE: Coordinating Tensor/Neuron-Level Sparsity with Expert Partition and Reconstruction

• [arxiv'25] BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference

• [SC'25] Diff-MoE: Efficient Batched MoE Inference with Priority-Driven Differential Expert Caching

• [arxiv'25] PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference

• [SC workshop'25] Compression Error Sensitivity Analysis for Different Experts in MoE Model Inference

• [SC workshop'25] Batch Tiling on Attention: Efficient Mixture of Experts Training on Wafer-Scale Processors

• [arxiv'25] Opportunistic Expert Activation: Batch-Aware Expert Routing for Faster Decode Without Retraining

• [arxiv'25] HybridEP: Scaling Expert Parallelism to Cross-Datacenter Scenario via Hybrid Expert/Data Transmission

• [arxiv'25] MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs

• [arxiv'25] ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts

• [arxiv'25] MergeMoE: Efficient Compression of MoE Models via Expert Output Merging

• [MICRO'25] Optimizing All-to-All Collective Communication with Fault Tolerance on Torus Networks

• [arxiv'25] GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models

• [MICRO'25] Stratum: System-Hardware Co-Design with Tiered Monolithic 3D-Stackable DRAM for Efficient MoE Serving

• [arxiv'25] Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting

• [arxiv'25] ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models

• [SOSP'25] KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models

• [arxiv'25] GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference

• [arxiv'25] MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE

• [arxiv'25] DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning

• [arxiv'25] Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts

• [NeurIPS'25] BrainMoE: Cognition Joint Embedding via Mixture-of-Expert Towards Robust Brain Foundation Model

• [NeurIPS'25] S’MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning

• [NeurIPS'25] The Omni-Expert: A Computationally Efficient Approach to Achieve a Mixture of Experts in a Single Expert Model

• [NeurIPS'25] MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE

• [NeurIPS'25] FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts

• [NeurIPS'25] FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training

• [NeurIPS'25] FlashMoE: Fast Distributed MoE in a Single Kernel [Code]

• [arxiv'25] Steering MoE LLMs via Expert (De)Activation

• [arxiv'25] HD-MoE: Hybrid and Dynamic Parallelism for Mixture-of-Expert LLMs with 3D Near-Memory Processing

• [arxiv'25] LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference

• [SC'25] MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?

• [arxiv'25] LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference

• [arxiv'25] LongCat-Flash Technical Report

• [arxiv'25] Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding

• [arxiv'25] HAP: Hybrid Adaptive Parallelism for Efficient Mixture-of-Experts Inference

• [arxiv'25] MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

• [SIGCOMM'25] MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism

• [ICLR'25] Ada-K Routing: Boosting the Efficiency of MoE-based LLMs

• [arxiv'25] Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

• [ICML'25] Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference

• [ICML'25] I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts

• [arxiv'25] Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models

• [SC'25] X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms

• [SIGCOMM'25] MixNet: A Runtime Reconfigurable Optical-Electrical Fabric for Distributed Mixture-of-Experts Training

• [ATC'25] PopFetcher: Towards Accelerated Mixture-of-Experts Training Via Popularity Based Expert-Wise Prefetch

• [arxiv'25] HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap

• [arxiv'25] PiKV: KV Cache Management System for Mixture of Experts

• [arxiv'25] BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs

• [arxiv'25] Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models

• [ACL'25] EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models

• [ACL'25] FOLDMOE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining

• [arxiv'25] The New LLM Bottleneck: A Systems Perspective on Latent Attention and Mixture-of-Experts

• [arxiv'25] Muon is Scalable for LLM Training

• [arxiv'25] Long-Tailed Distribution-Aware Router For Mixture-of-Experts in Large Vision-Language Model

• [arxiv'25] Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

• [arxiv'25] HarMoEny: Efficient Multi-GPU Inference of MoE Models

• [arxiv'25] Load Balancing Mixture of Experts with Similarity Preserving Routers

• [arxiv'25] MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing

• [arxiv'25] EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models

• [arxiv'25] CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning

• [arxiv'25] PreMoe: Lightening MoEs on Constrained Memory by Expert Pruning and Retrieval

• [arxiv'25] Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert Models

• [ATC'25] PopFetcher: Towards Accelerated Mixture-of-Experts Training Via Popularity Based Expert-Wise Prefetch

• [arxiv'25] Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony

• [ICML'25] FloE: On-the-Fly MoE Inference on Memory-constrained GPU

• [arxiv'25] PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning

• [arxiv'25] MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

• [arxiv'25] Faster MoE LLM Inference for Extremely Large Models

• [arxiv'25] Accelerating Mixture-of-Experts Training with Adaptive Expert Replication

• [NAACL'25] MoLA: MoE LoRA with Layer-wise Expert Allocation

• [NAACL'25] Marrying LLMs with Dynamic Forecasting: A Graph Mixture-of-expert Perspective

• [NAACL'25] Sparser Mixture-of-Adapters with Cross-Layer Generalization

• [NAACL'25] SimSMoE: Toward Efficient Training Mixture of Experts via Solving Representational Collapse

• [Mobicom'25] D2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving

• [arxiv'25] MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core

• [arxiv'25] MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching

• [arxiv'25] Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

• [arxiv'25] Unveiling Hidden Collaboration within Mixture-of-Experts in Large Language Models

• [arxiv'25] Dense Backpropagation Improves Training for Sparse Mixture-of-Experts

• [arxiv'25] MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints

• [arxiv'25] C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization for Test-Time Expert Re-Mixing

• [arxiv'25] Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models

• [arxiv'25] S'MoRE: Structural Mixture of Residual Experts for LLM Fine-tuning

• [DAC'25] HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference

• [arxiv'25] Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations

• [arxiv'25] HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

• [TKDE'25] A Survey on Mixture of Experts

• [ICLR'25] NetMoE: Accelerating MoE Training through Dynamic Sample Placement

• [arxiv'25] ProMoE: Fast MoE-based LLM Serving using Proactive Caching

• [arxiv'25] Mixture of Lookup Experts

• [EuroSys'25] Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores

• [EuroMLSys'25] Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference

• [EuroMLSys'25] Accelerating MoE Model Inference with Expert Sharding

• [arxiv'25] eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference

• [KDD'25] ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration

• [arxiv'25] Continual Pre-training of MoEs: How robust is your router?

• [arxiv'25] Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs

• [arxiv'25] Capacity-Aware Inference: Mitigating the Straggler Effect in Mixture of Experts

• [arxiv'25] Speculative MoE: Communication Efficient Parallel MoE Inference with Speculative Token and Expert Pre-scheduling

• [MLSys'25] Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts

• [arxiv'25] CoSMoEs: Compact Sparse Mixture of Experts

• [CVPR'25] DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models

• [ASPLOS'25] CoServe: Efficient Collaboration-of-Experts (CoE) Model Inference with Limited Memory

• [arxiv'25] Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts

• [arxiv'25] BigMac: A Communication-Efficient Mixture-of-Experts Model Structure for Fast Training and Inference

• [arxiv'25] DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs

• [arxiv'25] Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models

• [arxiv'25] MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing

• [arxiv'25] Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient

• [arxiv'25] Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models

• [arxiv'25] fMoE: Fine-Grained Expert Offloading for Large Mixture-of-Experts Serving

• [TPDS'25] EfficientMoE: Optimizing Mixture-of-Experts Model Training with Adaptive Load Balance

• [arxiv'25] Hecate: Unlocking Efficient Sparse Model Training via Fully Sharded Sparse Data Parallelism

• [NAACL'25] MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs

• [arxiv'25] BTS: Harmonizing Specialized Experts into a Generalist LLM

• [ASPLOS'25] FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

• [arxiv'25] Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

• [arxiv'25] Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

• [arxiv'25] Optimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing

• [MICRO'24] SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts

• [TPDS'24] MPMoE: Memory Efficient MoE for Pre-Trained Models With Adaptive Pipeline Parallelism

  • Journal version of [IPDPS'23] MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism

• [arxiv'24] DeepSeek-V3 Technical Report

• [arxiv'24] HEXA-MoE: Efficient and Heterogeneous-aware MoE Acceleration with ZERO Computation Redundancy

• [arxiv'24] Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation

• [arxiv'24] Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts

• [arxiv'24] ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

• [Survey 🔍] [arxiv'24] A Survey on Inference Optimization Techniques for Mixture of Experts Models

• [arxiv'24] DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

• [arxiv'24] Llama 3 Meets MoE: Efficient Upcycling

• [arxiv'24] Sparsing Law: Towards Large Language Models with Greater Activation Sparsity

• [arxiv'24] Mixture of A Million Experts

• [arxiv'24] MoE-CAP: Cost-Accuracy-Performance Benchmarking for Mixture-of-Experts Systems

• [arxiv'24] MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services

• [arxiv'24] Toward Inference-optimal Mixture-of-Expert Large Language Models

• [arxiv'24] Expert-Token Resonance: Redefining MoE Routing through Affinity-Driven Active Selection

• [MLArchSys'24 @ ISCA'24] MoE-ERAS: Expert Residency Aware Selection

• [arxiv'24] MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks

• [arxiv'24] Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing

• [arxiv'24] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

• [COLM'24] Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training

• [ME-FoMo @ ICLR'24] Scaling Laws for Fine-Grained Mixture of Experts

• [arxiv'24] UOE: Unlearning One Expert Is Enough For Mixture-of-experts LLMS

• [ML for Sys workshop @ NeurIPS'24] IFMoE: An Inference Framework Design for Fine-grained MoE

• [ML for Sys workshop @ NeurIPS'24] TurboMoE: Enhancing MoE Model Training with Smart Kernel-Fusion and Data Transformation

• [arxiv'24] Dense Backpropagation Improves Routing for Sparsely-Gated Mixture-of-Experts

• [arxiv'24] MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs

• [arxiv'24] Pro-Prophet: Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models

• [EMNLP'24] MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning

• [EMNLP'24] Mixture of Diverse Size Experts

• [EMNLP'24] AdaMOE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models

• [ACL'24] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

• [ACL'24] SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

• [SoCC'24] MoEsaic: Shared Mixture of Experts

• [KDD'24] Efficient Mixture of Experts based on Large Language Models for Low-Resource Data Preprocessing

• [arxiv'24] Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism

• [IPDPS'24] Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference

• [arxiv'24] EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference

• [arxiv'24] Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts

• [NeurIPS'24] Toward Efficient Inference for Mixture of Experts

• [arxiv'24] Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection

• [MLSys'24] SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models

• [SC'24] APTMoE: Affinity-Aware Pipeline Tuning for MoE Models on Bandwidth-Constrained GPU Nodes

• [NeurIPS'24] GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts

• [arxiv'24] HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference

• [arxiv'24] Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

• [NeurIPS'24] LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing

• [arxiv'24] Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

• [arxiv'24] Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts

• [NeurIPS'24] Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design

• [arxiv'24] ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference

• [arxiv'24] Demystifying the Compression of Mixture-of-Experts Through a Unified Framework

• [PML4LRS @ ICLR'24] Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

• [arxiv'24] Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling

• [arxiv'24] MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router

• [arxiv'24] Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models

• [arxiv'24] MoH: Multi-Head Attention as Mixture-of-Head Attention

• [arxiv'24] AT-MoE: Adaptive Task-planning Mixture of Experts via LoRA Approach

• [NeurIPS'24 (Splotlight)] Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts

• [arxiv'24] Aria: An Open Multimodal Native Mixture-of-Experts Model

• [arxiv'24] MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More

• [arxiv'24] MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

• [arxiv'24] Upcycling Large Language Models into Mixture of Experts

• [arxiv'24] No Need to Talk: Asynchronous Mixture of Language Models

• [arxiv'24] Lazarus: Resilient and Elastic Training of Mixture-of-Experts Models with Adaptive Expert Placement

• [arxiv'24] HMoE: Heterogeneous Mixture of Experts for Language Modeling

• [arxiv'24] FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

• [arxiv'24] AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

• [arxiv'24] Layerwise Recurrent Router for Mixture-of-Experts

• [arxiv'24] Partial Experts Checkpoint: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training

• [SRW @ ACL'24] MoExtend: Tuning New Experts for Modality and Task Extension

• [arxiv'24] MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts

• [arxiv'24] Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

• [arxiv'24] Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts

• [arxiv'24] Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models

• [ICML'24] Scaling Laws for Fine-Grained Mixture of Experts

• [ICML'24] Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training

• [MLSys'24] QMoE: Sub-1-Bit Compression of Trillion-Parameter Models

• [MLSys'24] Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping

• [arxiv'24] CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts

• [arxiv'24] AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

• [SIGIR'24] M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

• [EuroSys'24] ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling

• [arxiv'24] MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts

• [ICLR'24] Mixture of LoRA Experts

• [arxiv'24] Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

• [arxiv'24] MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

• [IJCAI'24] LocMoE: A Low-overhead MoE for Large Language Model Training

• [ISCA'24] Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

• [IPDPS'23] MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism

• [EMNLP'23] Adaptive Gating in Mixture-of-Experts based Language Models

• [ACL'23] AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation

• [ICLR'23] Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

• [ICML'23] Brainformers: Trading Simplicity for Efficiency

• [arxiv'23] Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

• [arxiv'23] Fast Inference of Mixture-of-Experts Language Models with Offloading

• [ATC'23] Accelerating Distributed MoE Training and Inference with Lina

• [ATC'23] SmartMoE: Efficiently Training Sparsely-Activated Models through Combining Offline and Online Parallelization

• [OSDI'23] Optimizing Dynamic Neural Networks with Brainstorm

• [SIGMOD'23] FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

• [ICS'23] A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training

• [MLSys'23] MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

• [MLSys'23] Tutel: Adaptive Mixture-of-Experts at Scale

• [arxiv'22] ST-MoE: Designing Stable and Transferable Sparse Expert Models

• [PPoPP'22] FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models

• [SustaiNLP @ EMNLP'22] Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production

• [NeurIPS'22] Mixture-of-Experts with Expert Choice Routing

• [ICML'22] DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

• [ICML'22] GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

• [JMLR'22] Switch transformers: scaling to trillion parameter models with simple and efficient sparsity

• [EMNLP'21] Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference

• [ICLR'17] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

• [EuroSys'26] Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering
• [arxiv'25] Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design
• [arxiv'25] FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models
• [arxiv'25] GPU-Initiated Networking for NCCL
• [SC'25] CPU- and GPU-initiated Communication Strategies for Conjugate Gradient Methods on Large GPU Clusters
• [SC'25] SDR-RDMA: Software-Defined Reliability Architecture for Planetary Scale RDMA Communication
• [HotNets'25] Photonic Rails in ML Datacenters
• [arxiv'25] DMA Collectives for Efficient ML Communication Offloads
• [arxiv'25] Collective Communication for 100k+ GPUs
• [arxiv'25] Uno: A One-Stop Solution for Inter- and Intra-Datacenter Congestion Control and Reliable Connectivity
• [SOSP'25] Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
• [MICRO'25] SuperMesh: Energy-Efficient Collective Communications for Accelerators
• [MICRO'25] SkipReduce: (Interconnection) Network Sparsity to Accelerate Distributed Machine Learning
• [MICRO'25] Optimizing All-to-All Collective Communication with Fault Tolerance on Torus Networks
• [arxiv'25] MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications
• [arxiv'25] Toward Co-adapting Machine Learning Job Shape and Cluster Topology
• [APNET'25] Rethinking Dynamic Networks and Heterogeneous Computing with Automatic Parallelization
• [arxiv'25] Efficient AllReduce with Stragglers
• [arxiv'25] TASP: Topology-aware Sequence Parallelism
• [NAIC @ SIGCOMM'25] Chronos: Prescheduled circuit switching for LLM training
• [arxiv'25] Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality
• [SIGCOMM'25] Falcon: A Reliable, Low Latency Hardware Transport
• [SIGCOMM'25] ByteScale: Communication-Efficient Scaling of LLM Training with a 2048K Context Length on 16384 GPUs
• [SIGCOMM'25] From ATOP to ZCube: Automated Topology Optimization Pipeline and A Highly Cost-Effective Network Topology for Large Model Training
• [SIGCOMM'25] Astral: A Datacenter Infrastructure for Large Language Model Training at Scale
• [SIGCOMM'25] ResCCL: Resource-Efficient Scheduling for Collective Communication
• [OSDI'25] ZEN: Empowering Distributed Training with Sparsity-driven Data Synchronization
• [OSDI'25] Enabling Efficient GPU Communication over Multiple NICs with FuseLink
• [arxiv'25] RoCE BALBOA: Service-enhanced Data Center RDMA for SmartNICs
• [arxiv'25] RailX: A Flexible, Scalable, and Low-Cost Network Architecture for Hyper-Scale LLM Training Systems
• [arxiv'25] Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
• [APNET'25] Congestion Control for AI Workloads with Message-Level Signaling
• [ASPLOS'25] Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning
• [ISCA'25] Chimera: Communication Fusion for Hybrid Parallelism in Large Language Models
• [arxiv'25] NoLoCo: No-all-reduce Low Communication Training Method for Large Models
• [arxiv'25] TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference
• [arxiv'25] FLASH: Fast All-to-All Communication in GPU Clusters
• [arxiv'25] MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules
• [arxiv'25] GenTorrent: Scaling Large Language Model Serving with An Overley Network
• [arxiv'25] Triton-distributed: Programming Overlapping Kernels on Distributed AI Systems with the Triton Compiler
• [arxiv'25] FlashOverlap: A Lightweight Design for Efficiently Overlapping Communication and Computation
• [arxiv'25] An Extensible Software Transport Layer for GPU Networking (UCCL) [Code]
• [HPCA'25] Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization
• [arxiv'25] HeteroPod: XPU-Accelerated Infrastructure Offloading for Commodity Cloud-Native Applications
• [Survey 🔍] [arxiv'25] GPU-centric Communication Schemes for HPC and ML Applications
• [EuroMLSys'25] TAGC: Optimizing Gradient Communication in Distributed Transformer Training
• [arxiv'25] UB-Mesh: a Hierarchically Localized nD-FullMesh Datacenter Network Architecture
• [MLSys'25] TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives
• [arxiv'25] Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo
• [NSDI'25] AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training
• [NSDI'25] Efficient Direct-Connect Topologies for Collective Communications
• [arxiv'25] InfinitePOD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching Transceivers
• [IEEE MICRO'25] Understanding and Characterizing Communication Characteristics for Distributed Transformer Models
• [arxiv'25] In-Network Preprocessing of Recommender Systems on Multi-Tenant SmartNICs
• [arxiv'25] Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
• [arxiv'25] The Power of Negative Zero: Datatype Customization for Quantized Large Language Models
• [arxiv'25] mFabric: An Efficient and Scalable Fabric for Mixture-of-Experts Training
• [NSDI'25] OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud
• [APNET'24] Understanding Communication Characteristics of Distributed Training
• [arxiv'24] TokenRing: An Efficient Parallelism Framework for Infinite-Context LLMs via Bidirectional Communication
• [arxiv'24] The Landscape of GPU-Centric Communication
• [arxiv'24] Revisiting the Time Cost Model of AllReduce
• [arxiv'24] LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale Model-in-Network Data-Parallel Training on Distributed GPUs
• [HotInfra'24] Immediate Communication for Distributed AI Tasks
• [NeurIPS'24] SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
• [SC'24] Optimizing Distributed ML Communication with Fused Computation-Collective Operations
• [SC'24] Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI
• [NeurIPS'24] LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
• [arxiv'24] LumosCore: Highly Scalable LLM Clusters with Optical Interconnect
• [TPDS'24] AutoDDL: Automatic Distributed Deep Learning With Near-Optimal Bandwidth Cost
• [HOTI'24] Unified Collective Communication (UCC): An Unified Library for CPU, GPU, and DPU Collectives
• [HOTI'24] Rail-only: A Low-Cost High-Performance Network for Training LLMs with Trillion Parameters
• [SC'24] Switch-Less Dragonfly on Wafers: A Scalable Interconnection Architecture based on Wafer-Scale Integration
• [HPDC'24] Near-Optimal Wafer-Scale Reduce
• [HPDC'24] Efficient all-to-all Collective Communication Schedules for Direct-connect Topologies
• [arxiv'24] HiCCL: A Hierarchical Collective Communication Library
• [ICS'24] gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters
• [ICS'24] Snoopie: A Multi-GPU Communication Profiler and Visualizer
• [arxiv'24] CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts
• [arxiv'24] Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping
• [arxiv'24] Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects
• [arxiv'24] Demystifying the Communication Characteristics for Distributed Transformer Models
• [ICPP'24] Sparse Gradient Communication with AlltoAll for Accelerating Distributed Deep Learning
• [NAIC @ SIGCOMM'24] Proof-of-Concept of a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation
• [NAIC @ SIGCOMM'24] Eloquent: A More Robust Transmission Scheme for LLM Token Streaming
• [NAIC @ SIGCOMM'24] OmNICCL: Zero-cost Sparse AllReduce with Direct Cache Access and SmartNICs
• [HotNets'24] I've Got 99 Problems But FLOPS Ain't One
• [HotNets'24] MLTCP: A Distributed Technique to Approximate Centralized Flow Scheduling For Machine Learning
• [HotNets'22] Congestion Control in Machine Learning Clusters
• [SIGCOMM'24] Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem
• [SIGCOMM'24] RDMA over Ethernet for Distributed Training at Meta Scale
• [SIGCOMM'24] Accelerating Model Training in Multi-cluster Environments with Consumer-grade GPUs
• [SIGCOMM'24] MCCS: A Service-based Approach to Collective Communication for Multi-Tenant Cloud
• [SIGCOMM'24] Crux: GPU-Efficient Communication Scheduling for Deep Learning Training
• [arxiv'24] MLTCP: Congestion Control for DNN Training
  • [HotNets'24] MLTCP: A Distributed Technique to Approximate Centralized Flow Scheduling For Machine Learning
• [arxiv'24] ForestColl: Efficient Collective Communications on Heterogeneous Network Fabrics
• [APNet'24] Understanding Communication Characteristics of Distributed Training
• [ICLR'24] ZeRO++: Extremely Efficient Collective Communication for Large Model Training
• [ICLR'24] CO2: Efficient Distributed Training with Full Communication-Computation Overlap
  • [arxiv] [openreview]
• [MLSys'24] L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep Learning
• [MLSys'24] Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
• [ASPLOS'24] T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives
• [ASPLOS'24] TCCL: Discovering Better Communication Paths for PCIe GPU Clusters
• [ASPLOS'24] Centauri: Enabling Efficient Scheduling for Communication-Computation Overlap in Large Model Training via Communication Partitioning
• [ASPLOS'24] Two-Face: Combining Collective and One-Sided Communication for Efficient Distributed SpMM
• [NSDI'24] THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
• [Survey 🔍] [arxiv'23] Communication-Efficient Distributed Deep Learning: A Comprehensive Survey
• [arxiv'23] Optimized Network Architectures for Large Language Model Training with Billions of Parameters
• [arxiv'23] FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
• [arxiv'23] Rethinking Memory and Communication Cost for Efficient Large Language Model Training
• [arxiv'23] Zen: Near-Optimal Sparse Tensor Synchronization for Distributed DNN Training
• [arxiv'23] Optimized Network Architectures for Large Language Model Training with Billions of Parameters
• [arxiv'23] TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training
• [INFOCOM'23] Libra: Contention-Aware GPU Thread Allocation for Data Parallel Training in High Speed Networks
• [ICDCS'23] bbTopk: Bandwidth-Aware Sparse Allreduce with Blocked Sparsification for Efficient Distributed Training
• [ICML'23] CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
  • Related to DT-FM (NeurIPS'22)
• [IPDPS'23] MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
• [ASPLOS'23] MSCCLang: Microsoft Collective Communication Language
• [ASPLOS'23] Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models
• [EuroSys'23] A2TP: Aggregator-aware In-network Aggregation for Multi-tenant Learning
• [MLSys'23] Cupcake: A Compression Optimizer for Scalable Communication-Efficient Distributed Training
• [MLSys'23] On Optimizing the Communication of Model Parallelism
• [NSDI'23] TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs
• [NSDI'23] Better Together: Jointly Optimizing ML Collective Scheduling and Execution Planning using SYNDICATE
• [NSDI'23] TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches
• [NSDI'23] ARK: GPU-driven Code Execution for Distributed Deep Learning
• [EuroSys'22] Out-of-order backprop: an effective scheduling technique for deep learning
• [ISCA'22] Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models
• [ISCA'22] Software-hardware co-design for fast and scalable training of deep learning recommendation models
• [SC'22] HammingMesh: A Network Topology for Large-Scale Deep Learning
• [PPoPP'22] Near-optimal sparse allreduce for distributed deep learning
• [MLSys'22] Synthesizing optimal parallelism placement and reduction strategies on hierarchical systems for deep learning (P^2)
• [ASPLOS'22] Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads (CoCoNET)
• [EuroSys'21] DGCL: an efficient communication library for distributed GNN training
• [ICLR'21] Multi-Level Local SGD for Heterogeneous Hierarchical Networks
• [SIGMOD'21] Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce [also in 2.5]
• [SC'21] Flare: flexible in-network allreduce
• [NSDI'21] Scaling Distributed Machine Learning with In-Network Aggregation
• [ISCA'21] Enabling compute-communication overlap in distributed deep learning training platforms
• [PPoPP'21] Synthesizing optimal collective algorithms (SCCL)
• [SIGCOMM'21] SiP-ML: High-Bandwidth Optical Network Interconnects for Machine Learning Training
• [ISCA'20] An in-network architecture for accelerating shared-memory multiprocessor collectives
• [NeurIPS'20] Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
• [PPoPP'20] Taming unbalanced training workloads in deep learning with partial collective operations
• [MLSys'20] Blink: Fast and Generic Collectives for Distributed ML
• [MLSys'20] PLink: Discovering and Exploiting Datacenter Network Locality for Efficient Cloud-based Distributed Training
• [OSDI'20] A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters (BytePS)
• [MLSys'19] Priority-based Parameter Propagation for Distributed DNN Training (P3)
• [MLSys'19] TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
• [SOSP'19] A generic communication scheduler for distributed DNN training acceleration (ByteScheduler)
• [ATC'17] Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

• [arxiv'25] FFTrainer: Fast Failover in Large-Language Model Training with Almost-Free State Management
• [arxiv'25] FailSafe: High-performance Resilient Serving
• [arxiv'25] GoCkpt: Gradient-Assisted Multi-Step overlapped Checkpointing for Efficient LLM Training
• [MICRO'25] Optimizing All-to-All Collective Communication with Fault Tolerance on Torus Networks
• [APSys'25] Indispensable CPU-centric Checkpointing for GPUs
• [CLUSTER'25] Capricorn: Efficient In-Memory Checkpointing for MoE Model Training with Dynamicity Awareness
• [arxiv'25] MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
• [arxiv'25] ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
• [arxiv'25] Efficient AllReduce with Stragglers
• [SOSP'25] Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
• [SOSP'25] Robust LLM Training Infrastructure at ByteDance
• [SC'25] LowDiff: Efficient Frequent Checkpointing via Low-Cost Differential for High-Performance Distributed Training Systems
• [OSDI'25] Understanding Stragglers in Large Model Training Using What-if Analysis
• [SIGMOD'25] Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
• [arxiv'25] Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication
• [ATC'25] SAVE: Software-Implemented Fault Tolerance for Model Inference against GPU Memory Bit Flips
• [ATC'25] Universal Checkpointing: A Flexible and Efficient Distributed Checkpointing System for Large-Scale DNN Training with Reconfigurable Parallelism
• [arxiv'25] Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation
• [arxiv'25] Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
• [arxiv'25] Characterizing GPU Resilience and Impact on AI/HPC Systems
• [NSDI'25] BCP: A Unified Checkpointing System for Large Foundation Model Development
• [NSDI'25] Minder: Faulty Machine Detection for Large-scale Distributed Model Training
• [EuroSys'25] SkyServe: Serving AI Models across Regions and Clouds with Spot Instances
• [ASPLOS'25] PCcheck: Persistent Concurrent Checkpointing for ML
• [arxiv'24] FALCON: Pinpointing and Mitigating Stragglers for Large-Scale Hybrid-Parallel Training
• [arxiv'24] MoEtion: Efficient and Reliable Checkpointing for Mixture-of-Experts Models at Scale
• [arxiv'24] MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training
• [arxiv'24] TrainMover: Efficient ML Training Live Migration with No Memory Overhead
• [arxiv'24] Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
• [arxiv'24] ByteCheckpoint: A Unified Checkpointing System for Large Foundation Model Development
• [arxiv'24] Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training
• [arxiv'24] Lazarus: Resilient and Elastic Training of Mixture-of-Experts Models with Adaptive Expert Placement
• [arxiv'24] PARALLELGPUOS: A Concurrent OS-level GPU Checkpoint and Restore System using Validated Speculation
• [SOSP'24] ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
• [HPDC'24] DataStates-LLM: Lazy Asynchronous Checkpointing for Large Language Models
• [EuroSys'24] Just-In-Time Checkpointing: Low Cost Error Recovery from Deep Learning Training Failures
• [NSDI'24] Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances
• [arxiv'23] Unicron: Economizing Self-Healing LLM Training at Scale
• [VLDB'23] Eficient Fault Tolerance for Recommendation Model Training via Erasure Coding
• [SOSP'23] GEMINI: Fast Failure Recovery in Distributed Training with In-Memory Checkpoints
• [SOSP'23] Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
• [NSDI'23] Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
• [EuroSys'22] Varuna: scalable, low-cost training of massive deep learning models
• [ATC'22] Sibylla: To Retry or Not To Retry on Deep Learning Job Failure
• [MLSys'21] Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
• [FAST'21] CheckFreq: Frequent, Fine-Grained DNN Checkpointing
• [ICSE'20] An Empirical Study on Program Failures of Deep Learning Jobs

• [SC'25] HELM: Characterizing Unified Memory Accesses to Improve GPU Performance under Memory Oversubscription
• [SC'25] MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall
• [arxiv'25] CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
• [arxiv'25] Reducing GPU Memory Fragmentation via Spatio-Temporal Planning for Efficient Large-Scale Model Training
• [ISCA'25] Forest: Access-aware GPU UVM Management
• [EuroSys'25] MEPipe: Democratizing LLM Training with Memory-Efficient Slice-Level Pipeline Scheduling on Cost-Effective Accelerators
• [EuroSys'25] Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
• [FAST'25 WiP] Baton: Orchestrating GPU Memory for LLM Training on Heterogeneous Cluster
• [CGO'25] IntelliGen: Instruction-Level Auto-tuning for Tensor Program with Monotonic Memory Optimization
• [arxiv'25] Memory Analysis on the Training Course of DeepSeek Models
• [IJCAI'24] LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
• [MICRO'24] SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts
• [arxiv'24] Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator
• [TACO'24] ATP: Achieving Throughput Peak for DNN Training via Smart GPU Memory Management
• [ICML'24] GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
• [ASPLOS'24] GMLake: Efficient and Transparent GPU Memory Defragmentation for Large-scale DNN Training with Virtual Memory Stitching
• [arxiv'23] Rethinking Memory and Communication Cost for Efficient Large Language Model Training
• [arxiv'23] Quantized Distributed Training of Large Models with Convergence Guarantees (QSDP)
• [arxiv'23] Does compressing activations help model parallel training?
• [SoCC'23] Towards GPU Memory Efficiency for Distributed Training at Scale
• [VLDB'23] PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
• [SOSP'23] Efficient Memory Management for Large Language Model Serving with PagedAttention
• [HPCA'23] MPress: Democratizing Billion-Scale Model Training on Multi-GPU Servers via Memory-Saving Inter-Operator Parallelism
• [HPCA'23] Tensor Movement Orchestration in Multi-GPU Training Systems
• [IJCAI'23] OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning
• [ICLR'22] LoRA: Low-Rank Adaptation of Large Language Models
  • algorithmic method for memory efficiency
• [VLDB'22] Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers
• [ATC'21] ZeRO-Offload: Democratizing Billion-Scale Model Training
• [ICLR'21] ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
• [ICLR'21] Dynamic Tensor Rematerialization
• [SC'21] ZeRO-infinity: breaking the GPU memory wall for extreme scale deep learning
• [HPCA'21] Sentinel: Efficient Tensor Migration and Allocation on Heterogeneous Memory Systems for Deep Learning
• [MLSys'20] Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
• [ASPLOS'20] Capuchin: Tensor-based GPU Memory Management for Deep Learning
• [ASPLOS'20] SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping
• [ESEC/FSE'20] Estimating GPU memory consumption of deep learning models
• [SC'20] ZeRO: memory optimizations toward training trillion parameter models
• [ISCA'18] Gist: Efficient Data Encoding for Deep Neural Network Training
• [PPoPP'18] Superneurons: dynamic GPU memory management for training deep neural networks
• [MICRO'16] vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design
• [arxiv'16] Training Deep Nets with Sublinear Memory Cost

• [SC workshop'25] WAGES: Workload-Aware GPU Sharing System for Energy-Efficient Serverless LLM Serving
• [SOSP'25] LithOS: An Operating System for Efficient Machine Learning on GPUs
• [arxiv'25] Towards Efficient and Practical GPU Multitasking in the Era of LLM
• [arxiv'25] Prism: Unleashing GPU Sharing for Cost-Efficient Multi-LLM Serving
• [OSDI'25] XSched: Preemptive Scheduling for Diverse XPUs
• [EuroSys'25] Improving GPU Sharing Performance through Adaptive Bubbleless Spatial-Temporal Sharing
• [PPOPP'25] SGDRC: Software-Defined Dynamic Resource Control for Concurrent DNN Inference on NVIDIA GPUs
• [arxiv'24] PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers
• [SC'24] ParvaGPU: Efficient Spatial GPU Sharing for Large-Scale DNN Inference in Cloud Environments
• [arxiv'24] Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads
• [ICPP'24] MIGER: Integrating Multi-Instance GPU and Multi-Process Service for Deep Learning Clusters
• [ASPLOS'24] RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing
• [EuroSys'24] Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications
• [ATC'23] Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent
• [NSDI'23] Transparent GPU Sharing in Container Clouds for Deep Learning Workloads
• [ICPP'23] FaST-GShare: Enabling Efficient Spatio-Temporal GPU Sharing in Serverless Computing for Deep Learning Inference
• [arxiv'23] GACER: Granularity-Aware ConcurrEncy Regulation for Multi-Tenant Deep Learning
• [arxiv'23] MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
• [SoCC'22] MISO: exploiting multi-instance GPU capability on multi-tenant GPU clusters
• [PACT'22] GPUPool: A Holistic Approach to Fine-Grained GPU Sharing in the Cloud
• [ATC'21] Zico: Efficient GPU Memory Sharing for Concurrent DNN Training
• [MLSys'20] Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
• [OSDI'20] AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
• [OSDI'20] PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications
• [RTAS'19] Fractional GPUs: Software-Based Compute and Memory Bandwidth Reservation for GPUs

• [arxiv'25] Tawa: Automatic Warp Specialization for Modern GPUs with Asynchronous References
• [arxiv'25] Dato: A Task-Based Programming Model for Dataflow Accelerators
• [arxiv'25] Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
• [NeurIPS'25] REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
• [SOSP'25] Mercury: Unlocking Multi-GPU Operator Optimization for LLMs via Remote Memory Scheduling
• [MICRO'25] StreamTensor: Make Tensors Stream in Dataflow Accelerators for LLMs
• [OSDI'25] PipeThreader: Software-Defined Pipelining for Efficient DNN Execution
• [OSDI'25] QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach
• [OSDI'25] Mirage: A Multi-Level Superoptimizer for Tensor Programs
• [OSDI'25] KPerfIR: Towards a Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads
• [arxiv'25] TileLang: A Composable Tiled Programming Model for AI Systems
• [arxiv'25] Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis
• [arxiv'25] DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training
• [ASPLOS'25] Mosaic: Exploiting Instruction-Level Parallelism on Deep Learning Accelerators with iTex Tessellation
• [ASPLOS'25] Concerto: Automatic Communication Optimization and Scheduling for Large-Scale Deep Learning
• [arxiv'25] Hercules: A Compiler for Productive Programming of Heterogeneous Systems
• [CC'25] LLM Compiler: Foundation Language Models for Compiler Optimization
• [CGO'25] IntelliGen: Instruction-Level Auto-tuning for Tensor Program with Monotonic Memory Optimization
• [SOSP'24] Scaling Deep Learning Computation over the Inter-core Connected Intelligence Processor with T10
• [OSDI'23] Cocktailer: Analyzing and Optimizing Dynamic Control Flow in Deep Learning
• [OSDI'23] Welder: Scheduling Deep Learning Memory Access via Tile-graph
• [OSDI'23] Effectively Scheduling Computational Graphs of Deep Neural Networks toward Their Domain-Specific Accelerators
• [OSDI'23] EINNET: Optimizing Tensor Programs with Derivation-Based Transformations
• [OSDI'23] Optimizing Dynamic Neural Networks with Brainstorm
• [OSDI'22] ROLLER: Fast and Efficient Tensor Compilation for Deep Learning
• [OSDI'20] Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks
• [OSDI'20] Ansor: Generating High-Performance Tensor Programs for Deep Learning
• [ASPLOS'20] FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System
• [OSDI'18] TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

• [EuroSys'26] Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering
• [arxiv'25] Flash Multi-Head Feed-Forward Network
• [arxiv'25] Iris: First-Class Multi-GPU Programming Experience in Triton
• [arxiv'25] AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
• [arxiv'25] ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels
• [SC'25] HyTiS: Hybrid Tile Scheduling for GPU GEMM with Enhanced Wave Utilization and Cache Locality
• [SC'25] UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-Tiling
• [arxiv'25] HipKittens: Fast and Furious AMD Kernels
• [TACO'25] HuntKTm: Hybrid Scheduling and Automatic Management for Efficient Kernel Execution on Modern GPUs
• [NeurIPS'25] FlashMoE: Fast Distributed MoE in a Single Kernel
• [MLSys'25] FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
• [arxiv'25] LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving
• [arxiv'25] TileLang: A Composable Tiled Programming Model for AI Systems
• [PLDI'25] Task-Based Tensor Computations on Modern GPUs
• [TACO'25] Kitsune: Enabling Dataflow Execution on GPUs
• [ICLR'25] ThunderKittens: Simple, Fast, and Adorable Kernels
• [PLDI'25] Task-Based Tensor Computations on Modern GPUs
• [ASPLOS'25] Composing Distributed Computations Through Task and Kernel Fusion
• [MLSys'25] FastTree: Optimizing Attention Kernel and Runtime for Tree-Structured LLM Inference
• [arxiv'24] ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs
• [arxiv'24] Flex Attention: A Programming Model for Generating Optimized Attention Kernels
• [NeurIPS'24] FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
• [ICLR'24] FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• [CGO'24] A Framework for Fine-Grained Synchronization of Dependent GPU Kernels
• [RTAS'24] Demystifying NVIDIA GPU Internals to Enable Reliable GPU Management
  • slides: link
• [arxiv'23] Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU
• [OSDI'23] Welder: Scheduling Deep Learning Memory Access via Tile-graph
• [arxiv'21] Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads
• [SIGMETRICS'21] Demystifying the Placement Policies of the NVIDIA GPU Thread Block Scheduler for Concurrent Kernels
• [NeurIPS'20] Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
• [NeurIPS'22] FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
• [RTSS'17] GPU Scheduling on the NVIDIA TX2: Hidden Details Revealed

• [SC'25] UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-Tiling
• [SC'25] RingX: Scalable Parallel Attention for Long-Context Learning on HPC
• [arxiv'25] Optimizing Long-context LLM Serving via Fine-grained Sequence Parallelism
• [NeurIPS'25] StarTrail: Concentric Ring Sequence Parallelism for Efficient Near-Infinite-Context Transformer Model Training
• [arxiv'25] Long-Context Attention Benchmark: From Kernel Efficiency to Distributed Context Parallelism
• [arxiv'25] Efficient Long-context Language Model Training by Core Attention Disaggregation
• [SOSP'25] DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
• [arxiv'25] Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
• [arxiv'25] Strata: Hierarchical Context Caching for Long Context Language Model Serving
• [arxiv'25] TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving
• [ACL'25] MiniKV: Pushing the Limits of 2-Bit KV Cache via Compression and System Co-Design for Efficient Long Context Inference
• [arxiv'25] HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism
• [arxiv'25] SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling
• [arxiv'25] Training Long-Context LLMs Efficiently via Chunk-wise Optimization
• [arxiv'25] SlimPipe: Memory-Thrifty and Efficient Pipeline Parallelism for Long-Context LLM Training
• [ASPLOS'25] FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
• [arxiv'25] XAttention: Block Sparse Attention with Antidiagonal Scoring
• [arxiv'25] SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
• [arxiv'25] ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
• [arxiv'25] Long-Context Inference with Retrieval-Augmented Speculative Decoding
• [PODC'25] System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
• [arxiv'25] ParallelComp: Parallel Long-Context Compressor for Length Extrapolation
• [arxiv'25] LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
• [arxiv'25] MoBA: Mixture of Block Attention for Long-Context LLMs
• [arxiv'25] Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
• [arxiv'25] APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs
• [SIGMOD'25] MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
• [arxiv'25] Twilight: Adaptive Attention Sparsity with Hierarchical Top-p Pruning
• [arxiv'25] Adjoint sharding for very long context training of state space models
• [arxiv'24] LoL-PIM: Long-Context LLM Decoding with Scalable DRAM-PIM System
• [arxiv'24] Data-Centric and Heterogeneity-Adaptive Sequence Parallelism for Efficient LLM Training
• [ICLR'24] Efficient Streaming Language Models with Attention Sinks [Code]
• [SOSP'24] LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism
• [arxiv'24] USP: A Unified Sequence Parallelism Approach for Long Context Generative AI
• [arxiv'24] Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer
• [NeurIPS'24 Workshop] Long Context RAG Performance of Large Language Models
• [arxiv'24] ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference
• [arxiv'24] Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations
• [arxiv'24] CSPS: A Communication-Efficient Sequence-Parallelism based Serving System for Transformer based Models with Long Prompts
• [COLM'24] TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding
• [arxiv'24] FocusLLM: Scaling LLM's Context by Parallel Decoding
• [Survey 🔍] [IJCAI'24] X-former Elucidator: Reviving Efficient Attention for Long Context Language Modeling

For comprehensive list of quantization papers, refer to https://github.com/Efficient-ML/Awesome-Model-Quantization.

• [arxiv'25] Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization
• [EMNLP'25] Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems
• [NeurIPS'25] 70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float (DFloat11)
• [arxiv'25] MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
• [CLUSTER'25] SplitQuant: Resource-Efficient LLM Offline Serving on Heterogeneous GPUs via Phase-Aware Model Partition and Adaptive Quantization
• [JMLR'25] BitNet: 1-bit Pre-training for Large Language Models
• [OSDI'25] DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization
• [arxiv'25] TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network
• [arxiv'25] DECA: A Near-Core LLM Decompression Accelerator Supporting Out-of-Order Invocation
• [arxiv'25] ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition
• [ISCA'25] Transitive Array: An Efficient GEMM Accelerator with Result Reuse
• [arxiv'24] Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
• [ICML'24] Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs
• [ACL'23] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
• [ICLR'23] GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• [OSDI'23] AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models
• [EuroSys'23] Hi-Speed DNN Training with Espresso: Unleashing the Full Potential of Gradient Compression with Near-Optimal Usage Strategies
• [ICML'22] TSPipe: Learn from Teacher Faster with Pipelines

• [VLDB'25] PS-MI: Accurate, E!icient, and Private Data Valuation in Vertical Federated Learning
• [arxiv'24] FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts
• [MLSys'24] LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning
• [arxiv'24] FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
• [KDD'24] FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
• [CCGrid'24] Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
• [EuroSys'24] Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy
• [arxiv'24] Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
• [SAC'24] Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
• [arxiv'23] CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
• [arxiv'23] Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
• [IMWUT'23] AttFL: A Personalized Federated Learning Framework for Time-series Mobile and Embedded Sensor Data Processing
• [Survey 🔍] [FGCS'23] Model aggregation techniques in federated learning: A comprehensive survey
• [SoCC'23] Auxo: Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering
• [MLSys'23] GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
• [WWW'23] To Store or Not? Online Data Selection for Federated Learning with Limited Storage
• [EuroSys'23] REFL: Resource-Efficient Federated Learning
• [VLDB'23] FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
• [RecSys'22] Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
• [TMLR'22] Optimal Client Sampling for Federated Learning
• [ICML'22] FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
• [MobiSys'22] FedBalancer: data and pace control for efficient federated learning on heterogeneous clients
• [MobiCom'22] PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning
• [MLSys'22] PAPAYA: Practical, Private, and Scalable Federated Learning
• [AISTATS'22] Federated Learning with Buffered Asynchronous Aggregation
• [NeurIPS'21] Federated Reconstruction: Partially Local Federated Learning
• [NeurIPS'21] FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
• [OSDI'21] Oort: Efficient Federated Learning via Guided Participant Selection
• [MICRO'21] AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
• [MLSys'19] Towards Federated Learning at Scale: System Design
• [Survey 🔍] [ACM CSUR'22] Federated Learning for Smart Healthcare: A Survey

• [CCS'25] MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
• [USENIX Security'25] Phantom: Privacy-Preserving Deep Neural Network Model Obfuscation in Heterogeneous TEE and GPU System
• [ASPLOS'24] LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
• [NeurIPS'24] Nimbus: Secure and Efficient Two-Party Inference for Transformers
• [ACL'24] SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC
• [S&P'24] BOLT: Privacy-Preserving, Accurate and Efficient Inference for Transformers
• [DAC'23] Privacy-Preserving DNN Training with Prefetched Meta-Keys on Heterogeneous Neural Network Accelerators
• [ICLR'23] MPCFormer: fast, performant and private Transformer inference with MPC
• [NeurIPS'22] Iron: Private Inference on Transformers

• [ASPLOS'25] Towards End-to-End Optimization of LLM-based Applications with Ayo
• [arxiv'24] APIServe: Efficient API Support for Large-Language Model Inferencing
• [OSDI'24] ChameleonAPI: Automatic and Efficient Customization of Neural Networks for ML Applications
• [ICML'22] Efficient Online ML API Selection for Multi-Label Classification Tasks (FrugalMCT)
• [NeurIPS'20] FrugalML: How to use ML Prediction APIs more accurately and cheaply

• [arxiv'25] AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
• [arxiv'25] ASAP: an Agentic Solution to Auto-optimize Performance of Large-Scale LLM Training
• [NeurIPS'25] REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
• [arxiv'25] Barbarians at the Gate: How AI is Upending Systems Research [Code]
• [arxiv'25] SuperCoder: Assembly Program Superoptimization with Large Language Models
• [HotOS'25] How I learned to stop worrying and love learned OS policies
• [VLDB'25] E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
• [SenSys'25] CheckMate: LLM-Powered Approximate Intermittent Computing
• [ICSE'25] Large Language Models as Configuration Validators
• [NeurIPS'24] IaC-Eval: A code generation benchmark for Infrastructure-as-Code programs
• [arxiv'24] Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
• [arxiv'24] LLMTune: Accelerate Database Knob Tuning with Large Language Models
• [SIGCOMM'24] NetLLM: Adapting Large Language Models for Networking
• [arxiv'24] LLM-Enhanced Data Management
• [arxiv'24] MPIrigen: MPI Code Generation through Domain-Specific Language Models
• [arxiv'24] Can Large Language Models Write Parallel Code?
• [arxiv'23] LLM-Assisted Code Cleaning For Training Accurate Code Generators
• [arxiv'23] Large Language Models for Compiler Optimization
• [VLDB'23] How Large Language Models Will Disrupt Data Management

• [NeurIPS'25] CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
• [MICRO'25] SuperMesh: Energy-Efficient Collective Communications for Accelerators
• [MICRO'25] Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
• [arxiv'25] VoltanaLLM: Feedback-Driven Frequency Control and State-Space Routing for Energy-Efficient LLM Serving
• [arxiv'25] GreenLLM: SLO-Aware Dynamic Frequency Scaling for Energy-Efficient LLM Serving
• [arxiv'25] Power Stabilization for AI Training Datacenters
• [arxiv'25] The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
• [arxiv'25] EcoServe: Enabling Cost-effective LLM Serving with Proactive Intra- and Inter-Instance Orchestration
• [NSDI'25] GREEN: Carbon-efficient Resource Scheduling for Machine Learning Clusters
• [HPCA'25] throttLL'eM: Predictive GPU Throttling for Energy Efficient LLM Inference Serving
• [arxiv'25] EcoServe: Designing Carbon-Aware AI Inference Systems
• [arxiv'25] Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends
• [arxiv'24] GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions
• [arxiv'24] EaCO: Resource Sharing Dynamics and Its Impact on Energy Efficiency for DNN Training
• [arxiv'24] DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency
• [SOSP'24] Perseus: Removing Energy Bloat from Large Model Training
• [arxiv'23] CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
• [ATC'23] EnvPipe: Performance-preserving DNN Training Framework for Saving Energy
• [NSDI'23] Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

• [ICDE'25] SAGE: A Framework of Precise Retrieval for RAG
• [SOSP'25] HedraRAG: Co-Optimizing Generation and Retrieval for Heterogeneous RAG Workflows
• [ISCA'25] HeterRAG: Heterogeneous Processing-in-Memory Acceleration for Retrieval-augmented Generation
• [arxiv'25] Patchwork: A Unified Framework for RAG Serving
• [arxiv'25] Accelerating Retrieval-Augmented Language Model Serving with Speculation
• [arxiv'25] RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving
• [arxiv'25] Long-Context Inference with Retrieval-Augmented Speculative Decoding
• [VLDB'25] Chameleon: a heterogeneous and disaggregated accelerator system for retrieval-augmented language models
• [arxiv'24] Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference
• [arxiv'24] RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation
• [arxiv'24] Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation
• [arxiv'24] Accelerating Retrieval-Augmented Language Model Serving with Speculation
• [NeurIPS'24 Workshop] Long Context RAG Performance of Large Language Models

• [arxiv'25] Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs
• [MICRO'25] PyTorchSim: A Comprehensive, Fast, and Accurate NPU Simulation Framework
• [MICRO'25] Swift and Trustworthy Large-Scale GPU Simulation with Fine-Grained Error Modeling and Hierarchical Clustering
• [arxiv'25] Frontier: Simulating the Next Generation of LLM Inference Systems
• [NAIC @ SIGCOMM'25] MLSynth: Towards Synthetic ML Traces
• [NAIC @ SIGCOMM'25] Simulating LLM training workloads for heterogeneous compute and network infrastructure
• [arxiv'25] Frontier: Simulating the Next Generation of LLM Inference Systems
• [arxiv'25] Maya: Optimizing Deep Learning Training Workloads using Emulated Virtual Accelerators
• [NSDI'25] Accelerating Design Space Exploration for LLM Training Systems with Multi-experiment Parallel Simulation
• [ASPLOS'25] Forecasting GPU Performance for Deep Learning Training and Inference
• [MLSys'24] Vidur: A Large-Scale Simulation Framework For LLM Inference

• [arxiv'25] Measuring Agents in Production
• [arxiv'25] Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
• [arxiv'25] Aragog: Just-in-Time Model Routing for Scalable Serving of Agentic Workflows
• [arxiv'25] AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
• [ML for Systems @ NeurIPS'25] Agentic Bridge Framework: Closing the Gap Between Agentic Capability and Performance Benchmarks
• [arxiv'25] Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live
• [arxiv'25] Sherlock: Reliable and Efficient Agentic Workflow Execution
• [arxiv'25] A CPU-Centric Perspective on Agentic AI
• [SAA'25] Useful Agentic AI: A Systems Outlook
• [SAA'25] Toward Systems Foundations for Agentic Exploratio
• [SAA'25] Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
• [SAA'25] Cortex: Workflow-Aware Resource Pooling and Scheduling for Agentic Serving
• [SAA'25] Tetris: Efficient and Predictive KV Cache Offloading for Agentic and Reasoning Workloads
• [SAA'25] GPU Memory Prediction for Multimodal Model Training
• [SAA'25] DMAS-Forge: A Framework for Transparent Deployment of AI Applications as Distributed Systems
• [SAA'25] Automated Annotation Inference for MCP-based Agents
• [SAA'25] EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models
• [SAA'25] Unified Agentic Interfaces is All You Need for AI Agent Observability
• [arxiv'25] Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution
• [arxiv'25] MobiAgent: A Systematic Framework for Customizable Mobile Agents
• [ICML'25] The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models
• [SIGCOMM'25] Intent-Driven Network Management with Multi-Agent LLMs: The Confucius Framework
• [arxiv'25] rStar2-Agent: Agentic Reasoning Technical Report
• [COLM'25] R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents
• [arxiv'25] Efficient and Scalable Agentic AI with Heterogeneous Systems
• [arxiv'25] Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC
• [arxiv'25] GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
• [ASPLOS'25] ReCA: Integrated Acceleration for Real-Time and Efficient Cooperative Embodied Autonomous Agents
• [arxiv'25] The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks
• [arxiv'24] AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
• [ICML'24] AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls

• [arxiv'25] ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
• [arxiv'25] RLHFSpec: Breaking the Efficiency Bottleneck in RLHF Training via Adaptive Drafting
• [arxiv'25] Fast LLM Post-training via Decoupled and Best-of-N Speculation
• [arxiv'25] Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter
• [arxiv'25] Beat the long tail: Distribution-Aware Speculative Decoding for RL Training
• [arxiv'25] WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library
• [arxiv'25] The Path Not Taken: RLVR Provably Learns Off the Principals
• [arxiv'25] AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
• [NeurIPS'25] Greedy Sampling Is Provably Efficient for RLHF
• [arxiv'25] Ask a Strong LLM Judge when Your Reward Model is Uncertain
• [arxiv'25] RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs
• [arxiv'25] Laminar: A Scalable Asynchronous RL Post-Training Framework
• [arxiv'25] The Art of Scaling Reinforcement Learning Compute for LLMs
• [arxiv'25] xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning
• [arxiv'25] Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Dense
• [arxiv'25] Learning from Failures: Understanding LLM Alignment through Failure-Aware Inverse RL
• [arxiv'25] Spurious Rewards: Rethinking Training Signals in RLVR
• [arxiv'25] Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
• [arxiv'25] RL in the Wild: Characterizing RLVR Training in LLM Deployment
• [arxiv'25] APRIL: Active Partial Rollouts in Reinforcement Learning to tame long-tail generation
• [NeurIPS'25] AReaL: Asynchronous Reinforcement Learning for Efficient and Scalable Language Reasoning
• [arxiv'25] ToRL: Scaling Tool-Integrated RL
• [arxiv'25] VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
• [arxiv'25] Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
• [Survey 🔍] [arxiv'25] A Survey of Reinforcement Learning for Large Reasoning Models
• [arxiv'25] RewardDance: Reward Scaling in Visual Generation
• [arxiv'25] floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
• [arxiv'25] ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
• [arxiv'25] History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
• [COLM'25] Sample Efficient Preference Alignment in LLMs via Active Exploration
• [COLM'25] Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use
• [arxiv'25] SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling
• [arxiv'25] SPECS: Faster Test-Time Scaling through Speculative Drafts
• [arxiv'25] Balanced Actor Initialization: Stable RLHF Training of Distillation-Based Reasoning Models
• [COLM'25] Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback
• [arxiv'25] ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
• [IPDPS'25] FlexRLHF: A Flexible Placement and Parallelism Framework for Efficient RLHF Training
• [arxiv'25] GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• [ACL'25] RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations
• [arxiv'25] Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs
• [arxiv'25] Scaling RL to Long Videos
• [arxiv'25] Test-Time Training Done Right
• [arxiv'25] LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training
• [arxiv'25] On-Policy RL with Optimal Reward Baseline
• [arxiv'25] StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation
• [arxiv'25] DAPO: An Open-Source LLM Reinforcement Learning System at Scale
• [MLSys'25] ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation
• [arxiv'25] Reward Reasoning Model
• [arxiv'24] Optimizing RLHF Training for Large Language Models with Stage Fusion

https://github.com/friedrichor/Awesome-Multimodal-Papers

• [arxiv'25] MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping
• [SoCC'25] ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving
• [arxiv'25] FlowMM: Cross-Modal Information Flow Guided KV Cache Merging for Efficient Multimodal Context Inference
• [arxiv'25] OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
• [arxiv'25] Fast-dLLM v2: Efficient Block-Diffusion LLM
• [arxiv'25] Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding
• [arxiv'25] Mordal: Automated Pretrained Model Selection for Vision Language Models
• [arxiv'25] Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
• [arxiv'24] LlamaFusion: Adapting Pretrained Language Models for Multimodal Generation
• [Survey 🔍] [arxiv'24] A Survey of Resource-efficient LLM and Multimodal Foundation Models

• [MICRO'25] HLX: A Unified Pipelined Architecture for Optimized Performance of Hybrid Transformer-Mamba Language Models
• [MLSys'25] Marconi: Prefix Caching for the Era of Hybrid LLMs

• [arxiv'25] Cyclotron: Compilation of Recurrences to Distributed and Systolic Architectures
• [arxiv'25] Streaming Tensor Program: A streaming abstraction for dynamic parallelism
• [arxiv'25] OckBench: Measuring the Efficiency of LLM Reasoning
• [SC workshop'25] Roofline Analysis of Tightly-Coupled CPU-GPU Superchips: A Study on MI300A and GH200
• [NeurIPS'25] Spark Transformer: Reactivating Sparsity in FFN and Attention
• [MICRO'25] ORCHES: Orchestrated Test-Time-Compute-based LLM Reasoning on Collaborative GPU-PIM HEterogeneous System
• [arxiv'25] vAttention: Verified Sparse Attention
• [USENIX ;login:] Wafer-Scale AI Compute: A System Software Perspective
• [arxiv'25] Training Large Language Models To Reason In Parallel With Global Forking Tokens
• [arxiv'25] How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
• [arxiv'25] Slm-mux: Orchestrating small language models for reasoning
• [arxiv'25] Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
• [arxiv'25] Less is More: Recursive Reasoning with Tiny Networks
• [arxiv'25] ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models
• [arxiv'25] Rethinking Thinking Tokens: LLMs as Improvement Operators
• [arxiv'25] Generalized Parallel Scaling with Interdependent Generations
• [arxiv'25] Composer: A Search Framework for Hybrid Neural Architecture Design
• [arxiv'25] dParallel: Learnable Parallel Decoding for dLLMs
• [NeurIPS'25] Speculate Deep and Accurate: Lossless and Training-Free Acceleration for Offloaded LLMs via Substitute Speculative Decoding
• [arxiv'25] AI Factories: It's time to rethink the Cloud-HPC divide
• [arxiv'25] Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
• [arxiv'25] SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences
• [arxiv'25] Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs
• [arxiv'25] LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model
• [arxiv'25] DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
• [VLDB'25] Powerful GPUs or Fast Interconnects: Analyzing Relational Workloads on Modern GPUs
• [arxiv'25] Less Is More: Training-Free Sparse Attention with Global Locality for Efficient Reasoning
• [arxiv'25] Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
• [arxiv'25] LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
• [arxiv'25] Copilot Arena: A Platform for Code LLM Evaluation in the Wild
• [arxiv'25] ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
• [MICRO'25] Pimba: A Processing-in-Memory Acceleration for Post-Transformer Large Language Model Serving
• [CFAgentic @ ICML'25] LLMSELECTOR: Learning to Select Models in Compound AI Systems
• [arxiv'25] Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication
• [arxiv'25] Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations [Code]
• [ISCA'25] Meta’s Second Generation AI Chip: Model-Chip Co-Design and Productionization Experiences
• [ISCA'25] Debunking the CUDA Myth Towards GPU-based AI Systems
• [ISCA'25] UGPU: Dynamically Constructing Unbalanced GPUs for Enhanced Resource Efficiency
• [arxiv'25] SeerAttention-R: Sparse Attention Adaptation for Long Reasoning
• [arxiv'25] Reinforcement Pre-Training
• [arxiv'25] MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
• [NSDI'25] Optimizing RLHF Training for Large Language Models with Stage Fusion
• [arxiv'25] Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately
• [arxiv'25] Faster Video Diffusion with Trainable Sparse Attention
• [arxiv'25] SSR: Speculative Parallel Scaling Reasoning in Test-time
• [arxiv'25] Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
• [arxiv'25] Think Only When You Need with Large Hybrid-Reasoning Models
• [MLSys'25] Optimizing LLM Queries in Relational Data Analytics Workloads
• [arxiv'25] Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
• [arxiv'25] Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads
• [arxiv'25] Process Reward Models That Think
• [arxiv'25] Seed-Thinking-v1.5: Advancing Superb Reasoning Models with Reinforcement Learning
• [arxiv'25] Sleep-time Compute: Beyond Inference Scaling at Test-time
• [arxiv'25] SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
• [arxiv'25] Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
• [arxiv'25] OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens
• [arxiv'25] NotebookOS: A Notebook Operating System for Interactive Training with On-Demand GPUs
• [arxiv'25] Alchemist: Towards the Design of Efficient Online Continual Learning System
• [arxiv'25] Linear Attention for Efficient Bidirectional Sequence Modeling
• [arxiv'25] S*: Test Time Scaling for Code Generation
• [arxiv'25] Optimizing Model Selection for Compound AI Systems
• [arxiv'25] Copilot Arena: A Platform for Code LLM Evaluation in the Wild
• [arxiv'25] Efficient-vDiT: Efficient Video Diffusion Transformers with Attention Tile
• [arxiv'25] BARE: Combining Base and Instruction-Tuned Language Models for Better Synthetic Data Generation
• [arxiv'25] Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity
• [arxiv'25] Adaptive Semantic Prompt Caching with VectorQ
• [EuroSys'25] HybridFlow: A Flexible and Efficient RLHF Framework
• [arxiv'25] Measuring GPU utilization one level deeper
• [ASPLOS'25] PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption
• [arxiv'24] Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
• [arxiv'24] Debunking the CUDA Myth Towards GPU-based AI Systems
• [arxiv'24] XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
• [CPAL'24 (PMLR)] Jaxpruner: A Concise Library for Sparsity Research
• [arxiv'24] Scorch: A Library for Sparse Deep Learning
• [arxiv'24] Drowning in Documents: Consequences of Scaling Reranker Inference
• [arxiv'24] Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions
• [arxiv'24] Computational Bottlenecks of Training Small-scale Large Language Models
• [Survey 🔍] [arxiv'24] A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
• [NeurIPS'24] Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
• [arxiv'24] Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
• [arxiv'24] DroidSpeak: Enhancing Cross-LLM Communication
• [arxiv'24] Disaggregating Embedding Recommendation Systems with FlexEMR
• [arxiv'24] JudgeBench: A Benchmark for Evaluating LLM-based Judges
• [arxiv'24] You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
• [arxiv'24] Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
• [ATC'24] Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor
• [arxiv'23] Efficiently Programming Large Language Models using SGLang
• [MICRO'23] Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads
• [arxiv'23] Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• [arxiv'22] Training language models to follow instructions with human feedback

This repository is motivated by:

• https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning
• https://github.com/S-Lab-System-Group/Awesome-DL-Scheduling-Papers
• https://github.com/ganler/ResearchReading
• https://jeongseob.github.io/readings_mlsys.html
• https://github.com/chwan1016/awesome-gnn-systems
• https://github.com/ConnollyLeon/awesome-Auto-Parallelism