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To deploy an LLM with vLLM, first verify that the server’s operating system, Python version, GPU, and driver meet vLLM’s requirements for that accelerator. For the NVIDIA path, the official vllm/vllm-openai Docker image provides a starting point for an OpenAI-compatible API server; you supply the model, GPU access, model credentials if needed, port mapping, and shared memory configuration.
Check that the GPU server matches a supported platform
vLLM’s GPU installation guide documents Linux and Python 3.10–3.13, but accelerator requirements and installation steps vary by platform. Check the section for your actual hardware before installing or selecting a container. The guide covers NVIDIA CUDA, AMD ROCm, Intel XPU, and Apple Silicon; a command for one vendor is not a general-purpose GPU command. See vLLM’s GPU installation requirements.
NVIDIA GPUs
The current guide lists NVIDIA GPUs with compute capability 7.5 or higher as supported examples, including T4, RTX 20xx, A100, L4, H100, and B200. This is a support example list, not a promise that any one of these GPUs has enough memory or performance for a particular model or traffic level. Check the chosen model’s memory needs and validate the intended workload separately.
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AMD, Intel, and Apple Silicon
vLLM documents separate installation paths for AMD GPUs using ROCm, Intel XPU, and Apple Silicon. Verify the relevant hardware family, runtime, and supported versions in the platform-specific instructions; NVIDIA’s container invocation below does not apply to these systems.
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Confirm NVIDIA driver and kernel compatibility
For CUDA 13 images, vLLM’s current guide says normal operation requires an NVIDIA R580-or-newer driver and Linux kernel 4.15 or newer. It also describes compatibility modes for R535 and R570 on selected professional or datacenter GPUs, with different kernel minimums: Linux 3.10 for R535 compatibility and Linux 4.15 for R570. These are specific to the stated CUDA image and compatibility modes. Verify NVIDIA’s live requirements against the server before deployment.
Start the NVIDIA server with the official container
vLLM publishes vllm/vllm-openai, an official image for its OpenAI-compatible server. The following is the NVIDIA starting example from the vLLM GPU installation guide. Set HF_TOKEN in the shell first if the model requires Hugging Face authentication:
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export HF_TOKEN=your_hugging_face_token
docker run --runtime nvidia --gpus all
-v ~/.cache/huggingface:/root/.cache/huggingface
--env "HF_TOKEN=$HF_TOKEN"
-p 8000:8000
--ipc=host
vllm/vllm-openai:latest
--model Qwen/Qwen3-0.6BThe example makes all available NVIDIA GPUs visible, passes the Hugging Face token into the container, maps host port 8000 to container port 8000, mounts the host Hugging Face cache, and launches the specified model. Replace the example model with the model identifier you intend to serve. Use the corresponding documented device and runtime setup for AMD or Intel rather than adapting this NVIDIA command by guesswork.
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Shared memory for inference
The guide calls for either --ipc=host or a configured --shm-size. PyTorch uses shared memory for inter-process communication, particularly with tensor-parallel inference. If host IPC is unsuitable for your container setup, size and configure shared memory using the alternative supported by your deployment environment.
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Persist model weights and compile artifacts
The Hugging Face cache mount in the launch example preserves downloaded model weights across container starts. That is separate from vLLM’s compile cache: the stable Docker guide identifies ~/.cache/vllm as the default VLLM_CACHE_ROOT and describes mounting a persistent volume there to retain compile artifacts. For the default root container path, the documented mount point is /root/.cache/vllm.
Run as a non-root user when appropriate
The CUDA image runs as root by default for backward compatibility, but includes a vllm user with UID 2000 and GID 0. When using that account, place writable mounted model or cache paths under /home/vllm so the user can access them. The stable Docker guide documents this identity and path guidance: vLLM Docker deployment.
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What the example does not configure
The command is a documented launch starting point, not a complete production deployment. The cited vLLM guidance covers container launch, GPU visibility, model access, port mapping, shared memory, cache persistence, and container identity; it does not establish a complete security, network-hardening, monitoring, or scaling configuration. Treat those as separate operational decisions for your environment.
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