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To deploy an LLM with vLLM on a GPU server, first confirm that the host, accelerator, driver, and Python version match vLLM’s current platform-specific requirements. For a supported NVIDIA system, the most direct documented starting point is vLLM’s official vllm/vllm-openai container, launched with GPU access, model credentials, port mapping, and adequate shared memory. The command below is a starting example—not a production security or scaling blueprint.
Check whether the GPU server is supported
vLLM’s current GPU installation guide describes Linux installation and Python 3.10–3.13, with separate instructions for NVIDIA CUDA, AMD ROCm, Intel XPU, and Apple Silicon. Check the section for the actual accelerator and host before installing; the platforms do not share one universal setup. Read vLLM’s GPU installation requirements.
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NVIDIA CUDA
The 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 compatibility list, not a promise that any of these GPUs has enough memory or performance for a particular model or traffic level. Check the requirements for your intended model and workload separately.
For CUDA 13 images, the guide states that 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 and datacenter GPUs, with different kernel minimums: Linux 3.10 for R535 compatibility and Linux 4.15 for R570. These constraints are specific to those image and compatibility paths; verify NVIDIA’s current compatibility requirements for your server before deployment.
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AMD, Intel, and Apple Silicon
Use the corresponding vLLM platform instructions for AMD ROCm, Intel XPU, or Apple Silicon rather than applying the NVIDIA command below unchanged. The device access, runtime, supported hardware, and version requirements differ by platform. The installation guide is the place to verify the applicable setup.
Start the OpenAI-compatible server on NVIDIA
vLLM publishes the vllm/vllm-openai image for serving an OpenAI-compatible API. The following is the documented NVIDIA example; it exposes port 8000, makes GPUs available to the container, mounts the Hugging Face cache, passes a Hugging Face token, and enables host IPC:
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docker run --runtime nvidia --gpus all
-v ~/.cache/huggingface:/root/.cache/huggingface
--env "HF_TOKEN=$HF_TOKEN"
-p 8000:8000
--ipc=host
vllm/vllm-openai:latest
--model Qwen/Qwen3-0.6B- Make model access available. Set
HF_TOKENin the shell that launches Docker if the model requires Hugging Face authentication. The cache mount lets the container use files under the host’s Hugging Face cache. - Choose the model. The example requests
Qwen/Qwen3-0.6B. Replace it with the intended model identifier only after checking its access requirements and whether its memory needs fit the GPU and workload. - Map the API port.
-p 8000:8000maps container port 8000 to host port 8000. Change the host side if needed, and ensure clients can reach the chosen port under your network configuration. - Provide shared memory. The command uses
--ipc=host. vLLM also documents--shm-sizeas an alternative. PyTorch uses shared memory for inter-process communication, particularly for tensor-parallel inference, so do not omit this consideration when adapting the launch command.
For AMD or Intel, follow the vendor-specific vLLM container instructions, including the required device and runtime setup. The NVIDIA flags in this example are not a cross-platform recipe.
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Keep model files and compile artifacts across restarts
The Hugging Face cache mount and vLLM’s compile cache serve different purposes. The first preserves or reuses model weights; a separate persistent volume at vLLM’s default cache root can retain compile artifacts between container starts. The stable Docker guide identifies the default root-container path as /root/.cache/vllm. See vLLM’s stable Docker deployment guidance.
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For example, a named Docker volume can be mounted at the default path:
docker volume create vllm-cache
docker run --runtime nvidia --gpus all
-v ~/.cache/huggingface:/root/.cache/huggingface
-v vllm-cache:/root/.cache/vllm
--env "HF_TOKEN=$HF_TOKEN"
-p 8000:8000
--ipc=host
vllm/vllm-openai:latest
--model Qwen/Qwen3-0.6BThis illustrates the cache mount in the documented root-container layout; adapt paths and permissions to the identity you choose. A cache volume is not a replacement for the model-weight mount.
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Choose a container identity deliberately
The CUDA image runs as root by default for backward compatibility. vLLM also documents a built-in vllm user with UID 2000 and GID 0. If using that user, ensure any mounted model or cache directories it must write to are writable and live under /home/vllm; the root paths shown in the NVIDIA example are not automatically suitable for non-root operation. Consult the Docker guide for the supported user and mount configuration.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesWhat this launch command does not configure
The documented command is useful for getting the server process running, but it does not by itself establish a secure or production-ready deployment. The cited vLLM setup guidance does not provide a complete configuration for network hardening, authentication policy, monitoring, scaling, or reliability. Address those requirements in the surrounding infrastructure and deployment design rather than assuming the example covers them.
Likewise, the supported-GPU list does not determine how much VRAM a particular model needs. Model size, runtime settings, and workload all affect fit. Establish that fit for the target model and traffic through its specific requirements and workload testing; the platform documentation does not offer a universal memory recommendation or cost/performance comparison.
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