New pre-built vLLM Docker Images for NVIDIA DGX Spark

[dbsci]

🚀 New vLLM Docker Images for NVIDIA DGX Spark

Quickly publishing initial vLLM Docker images optimized for NVIDIA DGX Spark (Blackwell-ready, NCCL + PyTorch rebuilt).

Available images (so far):

• scitrera/dgx-spark-vllm:0.13.0-t4 — vLLM 0.13.0, PyTorch 2.9.1, CUDA 13.0.2, Transformers 4.57.5, Triton 3.5.1, NCCL 2.28.9-1
• scitrera/dgx-spark-vllm:0.14.0rc2-t4 — vLLM 0.14.0rc2, PyTorch 2.10.0-rc6, CUDA 13.1.0, Transformers 4.57.5, Triton 3.5.1, NCCL 2.28.9-1
• scitrera/dgx-spark-vllm:0.14.0rc2-t5 — vLLM 0.14.0rc2, PyTorch 2.10.0-rc6, CUDA 13.1.0, Transformers 5.0.0rc3, Triton 3.5.1, NCCL 2.28.9-1

Both images include Ray for multi-node / cluster deployments. Will be adding transformers 5 variants soon to enable use for GLM-4.6V.

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Example usage

docker run \
  --privileged \
  --gpus all \
  -it --rm \
  --network host --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  scitrera/dgx-spark-vllm:0.13.0-t4 \
  vllm serve \
    Qwen/Qwen3-1.7B \
    --gpu-memory-utilization 0.7

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Tag semantics

• -t4 → Transformers 4.x

  • Example: 0.13.0-t4 = vLLM 0.13.0 + Transformers 4.57.5

• -t5 → Transformers 5.x (pre-release)

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Inspecting package versions

Major component versions are embedded as Docker labels:

docker inspect scitrera/dgx-spark-vllm:0.14.0rc2-t4 \
  --format '{{json .Config.Labels}}' | jq

Example output:

{
  "dev.scitrera.cuda_version": "13.1.0",
  "dev.scitrera.flashinfer_version": "0.6.1",
  "dev.scitrera.nccl_version": "2.28.9-1",
  "dev.scitrera.torch_version": "2.10.0-rc6",
  "dev.scitrera.transformers_version": "4.57.5",
  "dev.scitrera.triton_version": "3.5.1",
  "dev.scitrera.vllm_version": "0.14.0rc2"
}

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Notes

• Updated NCCL (versus PyTorch 2.9.1)
• PyTorch, Triton, and vLLM are rebuilt accordingly
• These images are early / experimental

For faster iteration on vLLM, I’d recommend @eugr’s repo:
👉 https://github.com/eugr/spark-vllm-docker.

long-term maintenance, support, and feedback plans are still TBD

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