qwen3-coder-next - vllm 0.15.1 - transformers 5 - optimized for dgx spark

qwen3-vllm.sh

#!/bin/bash
docker run -d \                                                                                                                                                                     
    --name vllm \
    --restart unless-stopped \                                                                                                                                                        
    --gpus all \                                                                                                                                                                    
    --ipc host \
    --shm-size 64gb \
    --memory 110g \
    --memory-swap 120g \
    --pids-limit 4096 \
    -p 0.0.0.0:18080:8000 \
    -e HF_TOKEN="${HF_TOKEN:-}" \
    -e VLLM_LOGGING_LEVEL="INFO" \
    -e NVIDIA_TF32_OVERRIDE="1" \
    -e TORCH_ALLOW_TF32_CUBLAS_OVERRIDE="1" \
    -e VLLM_TORCH_COMPILE="1" \
    -e VLLM_FLOAT32_MATMUL_PRECISION="high" \
    -e VLLM_LOG_STATS_INTERVAL="10" \
    -e VLLM_ATTENTION_BACKEND="FLASHINFER" \
    -e VLLM_FLASHINFER_FORCE_TENSOR_CORES="1" \
    -e VLLM_FLASHINFER_MOE_BACKEND="throughput" \
    -e CUDA_VISIBLE_DEVICES="0" \
    -e PYTHONHASHSEED="0" \
    -e VLLM_USE_V2_MODEL_RUNNER="0" \
    -e VLLM_ENABLE_PREFIX_CACHING="1" \
    -e TORCH_CUDA_ARCH_LIST="12.1f" \
    -v $HOME/huggingface:/root/.cache/huggingface \
    scitrera/dgx-spark-vllm:0.15.1-t5 \
    vllm serve Qwen/Qwen3-Coder-Next-FP8 \
    --served-model-name qwen3-coder-next \
    --load-format fastsafetensors \
    --attention-backend flashinfer \
    --port 8000 \
    --max-model-len 262144 \
    --block-size 128 \
    --max-num-seqs 16 \
    --max-num-batched-tokens 131072 \
    --gpu-memory-utilization 0.80 \
    --kv-cache-dtype auto \
    --enable-prefix-caching \
    --trust-remote-code \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --disable-uvicorn-access-log \
    --kv-cache-metrics \
    --cudagraph-metrics \
    --enable-mfu-metrics \
    -cc.max_cudagraph_capture_size 512 \
    --tensor-parallel-size 1

I stopped the docker at 114GB, I will retry with -e VLLM_TORCH_COMPILE="0".
the next steps are --max-num-batched-tokens 32K and --gpu-memory-utilization 0.75.
I’m starting to think that with 128GB, the context to use should be 128K.