Qwen3 inference engine built on cuTile Rust 0.2.0.

• CUDA 13.2+

Place the model directories alongside this repo:

parent/
  grout/        # this repo
  hf_models/
    qwen3_4b/   # RTX 5090 / sm_120 benchmark model
    qwen3_32b/  # B200 / sm_100 benchmark model

Environment is configured via .cargo/config.toml:

cargo run --release -- \
  --model "../hf_models/qwen3_4b" \
  --prompt "Hello, how are you?" \
  --max-new-tokens 50

The first run compiles all cuTile kernels (MLIR -> PTX -> CUBIN). Subsequent runs use the kernel cache.

main.rs         CLI (clap + tokio)
config.rs       Qwen3Config deserialization from config.json
loader.rs       SafeTensors weight loading (mmap -> fp16 -> GPU)
kernels.rs      cuTile Rust GPU kernels (#[cutile::module])
cublas.rs       cuBLAS GEMM/GEMV wrapper (via cudarc)
model.rs        Qwen3Engine: StepGraph IR, forward pass, generation loop

The engine uses a StepGraph IR — a sequence of ops (GEMM, RmsNorm, RoPE, Attention, etc.) compiled once per sequence length class (prefill vs decode). For decode, the graph is captured as a CUDA graph for replay without CPU overhead.

Key execution path:

1. Prefill: Encode the full prompt in one pass (batched GEMM + flash attention)
2. CUDA graph capture: Run one decode step to capture the graph
3. Decode loop: Replay the captured graph, updating only token ID and position via memcpy_htod_async

All GPU kernels are written in cuTile Rust's DSL (#[cutile::module]), which compiles Rust to MLIR to PTX:

• embedding_batch_f16 — batched token embedding lookup
• rms_norm_f16 — RMS normalization
• add_rms_norm_f16 — fused residual add + RMS norm
• add_rms_norm_decode_raw_f16 — decode-specialized fused residual add + RMS norm
• rope_seq_f16 / rope_seq_dynpos_f16 — rotary position embeddings
• kv_cache_update_seq_f16 / kv_cache_update_seq_dynpos_f16 — KV cache write
• qk_norm_rope_kv_prefill_raw_f16 / qk_norm_rope_kv_decode_raw_f16 — fused Q/K norm + RoPE + KV-cache write paths
• flash_attn_causal_seq_f16, fmha_prefill_*, fmha_decode_gqa_split — prefill/decode attention kernels
• splitk_reduce_merge — decode split-K attention merge
• silu_mul_2d_f16 — SiLU activation * up projection
• add_2d_f16 — element-wise add
• argmax_blocks_f16 — block-parallel argmax for greedy decoding
• gather_row_f16 — single-row extraction

cuBLAS handles the linear projections (GEMM/GEMV) via cublas.rs.

The paper-facing benchmark harness lives in benchmarks/. It compares Grout against SGLang and vLLM by default, with llama.cpp and TRT-LLM available as opt-in baselines.

Run the current RTX 5090 / sm_120 sweeps from the repository root:

./benchmarks/sweep_tg_sm120.sh
./benchmarks/sweep_pp_sm120.sh

Run the B200 / sm_100 profile by overriding the model path as needed:

MODEL_HF=../hf_models/qwen3_32b ./benchmarks/sweep_tg_sm100.sh
MODEL_HF=../hf_models/qwen3_32b ./benchmarks/sweep_pp_sm100.sh

The sweep scripts use driver-controlled clocks by default. They still accept an optional MHz argument for debugging clock-locked runs, but paper runs should either leave clocks unlocked or explicitly disclose the lock policy.

The paper-facing numbers live in the sweep aggregates, not in manually updated tables. Use aggregate.csv for plots and aggregate.md for quick inspection.

• RTX 5090 / Qwen3-4B TG sweep: benchmarks/results/sweep/20260508_111703_plus_115728_tg8192/
• RTX 5090 / Qwen3-4B PP sweep: benchmarks/results/sweep/20260508_114340/
• B200 / Qwen3-32B final results: benchmarks/results/final/b200_qwen3_32b/

The B200 results are also indexed in benchmarks/RESULTS.md.

For a direct Grout-only run:

cargo build --release --features benchmarks --bin grout_bench
target/release/grout_bench \
  --model "../hf_models/qwen3_4b" \
  --prompt "Hello, how are you?" \
  --max-new-tokens 512 \
  --max-seq-len 4096 \
  --reps 10 \
  --warmup-reps 3 \
  --ignore-eos \
  --quiet

See benchmarks/README.md for benchmark policy, engine versions, and canonical run commands.