vLLM Installation and First Deployment

Prepare a GPU environment, install a compatible vLLM release, serve a documented model, and verify the API without exposing credentials.

By Hostnot GPU · 9/6/2026

vLLM is a self-managed model-serving runtime. Installing it on a dedicated GPU does not create a platform-managed serverless endpoint or imply that all OpenAI API features are supported. Start with a loopback-only server and one short request, then evaluate memory and concurrency before exposing a service.

Select a compatible release and model

Use Linux and a Python version, GPU architecture, host driver, and CUDA wheel combination supported by your selected vLLM release. Its binary extensions are coupled to specific PyTorch/CUDA builds. Use a fresh environment rather than installing it over an unrelated existing torch stack. Read the release's GPU installation instructions and model-support list. Native Windows is not the standard Linux installation path.

The command pattern below is grounded in vLLM v0.10.2 documentation; that version is a syntax reference, not a current security recommendation. Select an approved supported release, check its release notes and security updates, and confirm flags with its vllm serve --help. Choose the exact publisher repository and immutable revision, its license, and compatible tokenizer/chat template. Budget host RAM, download disk, weights, and KV-cache headroom.

Install in a dedicated environment

Run these only in your prepared workload environment. Set an approved exact version first. Installation and subsequent model loading may download large artifacts.

python3 -m venv .venv-vllm
. .venv-vllm/bin/activate
: "${VLLM_VERSION:?Set the approved exact vLLM version}"
python -m pip install "vllm==$VLLM_VERSION"
python -m pip check
vllm --version
vllm serve --help

The plain package command uses the selected release's standard wheel/dependencies. If your target needs an alternate CUDA wheel or architecture-specific build, use that release's official instructions instead. Do not independently upgrade torch afterward. Verify nvidia-smi and a small CUDA tensor operation before model startup.

Start a bounded local service

Set MODEL_ID to your approved supported chat repository and MODEL_REVISION to its immutable commit. The limits below are conservative test settings, not guaranteed to fit every model or GPU. Use a model whose documented context is at least 2,048 tokens.

: "${MODEL_ID:?Set a supported chat-model repository}"
: "${MODEL_REVISION:?Set its approved immutable commit}"
vllm serve "$MODEL_ID" --revision "$MODEL_REVISION" --tokenizer-revision "$MODEL_REVISION" --served-model-name local-smoke --host 127.0.0.1 --port 8000 --max-model-len 2048 --max-num-seqs 1 --gpu-memory-utilization 0.8

Wait for readiness rather than assuming model download means startup succeeded. A memory-utilization fraction is the engine's budgeting input, not a hard security isolation boundary or a guarantee against OOM. Do not run another workload in the remaining fraction without measurement and owner permission. Do not add --trust-remote-code simply to bypass an unsupported architecture; review any repository code first.

Verify discovery and generation

From the same host, query the local server, then send a bounded non-sensitive prompt. The served alias in the request must match the launch option.

curl --fail-with-body --max-time 10 http://127.0.0.1:8000/v1/models
curl --fail-with-body --max-time 120 http://127.0.0.1:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"local-smoke","messages":[{"role":"user","content":"Explain a GPU in one sentence."}],"max_tokens":64,"temperature":0,"stream":false}'

Expect a model entry named local-smoke and a response containing a choice, message content, and a finish reason. A length finish reason can mean the 64-token cap was reached. Missing chat-template errors require the exact publisher format; a base completion model is not automatically chat-ready. Publisher generation configuration can alter defaults, so record it when comparing results.

Troubleshoot and secure the next stage

For startup OOM, distinguish weight loading from cache profiling and lower context/concurrency only where relevant. For unsupported architecture or dtype, consult the selected version's support table rather than installing random kernels. For 401/403 during download, resolve model access using an approved secret store or interactive login, not an unreviewed mirror. A disconnected HTTP client does not by itself establish that server-side computation stopped.

Loopback prevents a public listener but is accessible to local users. For remote development use SSH forwarding. Before shared or public operation, require authentication, TLS, request-size limits, quotas, and bounded queues at the appropriate serving boundary. vLLM API-key support is not a complete multi-tenant security system, and some operational endpoints need separate protection. Keep secrets out of command history and request logs.

Measure cold load, warm latency, output length, peak memory, and error rate before raising concurrency. Idle serving still consumes rented time. Stop the foreground server with Ctrl+C, save outputs and the reviewed environment record, and terminate unused allocations through instance controls.