LMCache boosts PyTorch with LLM inference acceleration

Arcitecta plots next phase with denser database tech and HPC ambitions. Chris Mellor, Blocks and Files https://bit.ly/4ovpGmT Arcitecta, #MultiCloud #DataManagement #FileStorage #ObjectStorage #GFS #GNS #AI #HPC #ITPT The IT Press Tour ur 64th Edition in New York

Arcitecta plots next phase with denser database tech and HPC ambitions. Chris Mellor, Blocks and Files https://bit.ly/4ovpGmT Arcitecta, #MultiCloud #DataManagement #FileStorage #ObjectStorage #GFS #GNS #AI #HPC #ITPT The IT Press Tour ur 64th Edition in New York

Exciting time for LLM inference on Google Cloud TPUs! 🚀 Two powerful LLM inference frameworks are making it easier than ever to achieve high-performance, cost-effective serving: 1️⃣ vLLM on TPU The vLLM team just announced a new unified backend for TPUs! It's powered by tpu-inference, unifying JAX and PyTorch under a single, high-performance JAX lowering path. This means better performance, broader model support, and deeper, native TPU integration. 🔥 2️⃣ SGLang-JAX For those pushing the limits, this is a new, JAX-based inference engine engineered from the ground up specifically for TPUs. It's designed to deliver exceptional throughput and low latency for the most demanding serving workloads. ⚡ Check out the announcement and projects: https://lnkd.in/gRir_ZVp https://lnkd.in/gTipf7MJ Amazing to see this level of optimization and choice, making it easier than ever to scale generative AI cost-effectively on TPUs. #GoogleCloud #TPU #LLM #Inference #vLLM #SGLang #JAX #AI

🚀New Blog Series: Building a Production-Grade LLM Inference Platform on GKE Built an LLM app that works locally? Now it’s time to scale! Thousands of requests, sub-second latency, multi-GPU workloads. My new blog series shows how to build a production-grade LLM inference platform on GKE from scratch. We start with raw gcloud commands, then move to Terraform automation while explaining everything that happens under the hood. 📘 Part 1 kicks off with setting up GKE with GPU and RDMA support, the foundation for scalable, high-performance inference. 👉 Read Part 1: https://lnkd.in/gdFgVzu9 #LLM #GKE #MLOps #AIInfrastructure #Kubernetes #GoogleCloud Derrick Wong Andrew Do (Anh Duc) Injae Kwak Rajat Pandit Mitesh Agarwal Bobby Allen, MS, PMP Allan Naim Minjae Kang

vLLM Semantic Router × vLLM Production Stack — Work in progress 🚧 We’re working with the vLLM Production Stack team to integrate Semantic Router natively. Why they’re complementary? 🤔 vLLM Semantic Router ⇒ Intent Signals, Safety Guardrails, Domain Aware Prompt Optimization, Similarity Cache. vLLM Production Stack ⇒ vLLM (LLM Runtime) + LMCache (Distributed KVCache) + Kubernetes Native Stack + GPU Scheduler, AutoScaling, and LLM Observability. Result: Building System Intelligence Platform (together)! 🔥 Semantic Signal-Driven Dispatch ✕ Cache-Aware Execution/Distribution ✕ GPU-Aware Scheduling x Production Grade Inference vLLM Semantic Router: https://lnkd.in/gDnEJjvi vLLM Production Stack: https://lnkd.in/g3GAn6Rb Innovation thrives when great minds come together. Let us explore the next-gen full stack solution of System Intelligence in LLM Era. 🚀 #vLLM #SemanticRouter #ProductionStack #SystemIntelligence #LMCache #GPU #MLOps #Kubernetes #OpenSource #MLSys

In our last reel, we explored why compute is the biggest bottleneck for OpenAI, and how infrastructure may ultimately decide the pace of modern tools adoption. https://lnkd.in/gXXYMRja But there’s another challenge that doesn’t come from servers or chips. It comes from us. Voice cloning is now so convincing that scams are no longer hypothetical. For anyone with hours of content online, the risk is very real. Do you think we’ll adapt fast enough to handle the security risks of AI voices? Share your thoughts below.

Introduction to llm-d Open-source Kubernetes-native Framework for Distributed LLM Inference | Ep 140 #cloudnativefm I recently had a great conversation with the Red Hat team about llm_d, a new open-source effort that’s starting to tackle a problem we’re seeing more and more in production ML stacks: inference workloads becoming monolithic, heavy, and hard to scale. A few highlights from the discussion and why llm-d matters: Inspiration: llm-d draws a lot of inspiration from work done in projects like vLLM which optimized inference on everything from laptops to DGX clusters (caching, speculative decoding, distribution). The problem: Today we often run inference as one big container (model + runtime + observability + config + pipelines). When you scale, you end up copying too much state across nodes, inefficient and brittle. The idea: Treat the model and its runtime as disaggregated, first-class components inside Kubernetes. Break the container into parts (cache, prefill/decode, GPU-bound work, CPU-bound work) and let the platform place and scale each piece independently. Why it’s promising: cache- https://lnkd.in/gX6xrhcf

Vector databases quietly hold your most sensitive AI data — and unlike password hashes, embeddings are fully invertible. Database breach → reconstruct the original data. We built CyborgDB to fix this. 🔒 Encryption-in-use for vector workloads 🧩 Drop-in proxy for Postgres / Redis 🗝️ BYOK / HYOK ⚙️ Automatic index configuration — no more manual tuning headaches. 🎯 Visiting NVIDIA GTC DC next week? Stop by Booth I-8 to see: • Live reconstruction attack demos (yes, we’ll show you how embeddings leak) • Secure GPU-accelerated encrypted search at billion-vector scale • Sub-millisecond latency benchmarks with real workloads #NVIDIAGTC #GTC2025 #GTCDC #GTC #VectorDatabases #SecureAI #NVIDIA #CyborgDB #PrivacyPreservingAI

I’ve been spending some time with the DGX Spark running speculative decoding and building multi-agent chatbots on a single node. It’s not the fastest system. Inference speeds won’t blow anyone away. But it’s reliable, simple to spin up, and surprisingly uniform across the whole stack. You can move between TensorRT-LLM, llama.cpp, and the embedding servers without dealing with the usual CUDA or dependency issues. The real unlock is FP4. It isn’t perfect, and there’s a small accuracy drop compared to FP8, but it’s what makes everything work on a single machine. FP4 keeps floating-point range and structure while cutting memory use by more than half. That’s why I can load multiple large models, experiment with speculative decoding, and test agent workflows all locally. DGX Spark isn’t meant to host production workloads forever. It’s a development box, and it fills that role really well. The fact that I can prototype multi-agent systems and see how these components interact without cloud dependencies or extra setup feels like a real shift in how we build. This isn’t about chasing speed. It’s about how much easier it’s getting to explore complex systems with real tools that actually run. #DGXSpark #FP4 #TensorRTLLM #AgenticAI #AIEngineering #MultiAgentSystems

LLMs are an important generative #AI use case. Check out this performance brief to see the improvement in Accelerating Vector Database Performance through Disk-based Indexes with KIOXIA CD8P Series SSDs Deployed in an @HPE ProLiant DL360 Gen11 Server: https://bit.ly/3TuRESC