# Install vLLM: Check GPU, Driver and Package Compatibility

Install vLLM through a documented route after checking GPU, OS, driver, Python, package backend and model compatibility as separate layers.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/vllm-install-compatibility/
Language: en

vLLM installation starts with a supported platform and build, not a sequence of upgrades to every package. Compatibility depends on GPU, operating system, driver, Python and engine libraries. A successful Python import does not establish that the chosen model can execute on the accelerator.

For an equipment pilot, select one documented installation route and preserve exact versions so the result can be reproduced.

## Record the software environment

List GPU model and available VRAM, CPU architecture, OS, driver and the proposed execution route: Python environment or container. For several cards, preserve the individual configuration.

The official GPU guide provides hardware-specific routes. Do not apply NVIDIA instructions to AMD or assume a package with a suitable Python version contains the required backend. CPU architecture can also affect the availability of prebuilt images.

## Separate the driver from package libraries

| Layer | Question to verify |
| --- | --- |
| Host driver | Does it support the actual device and route? |
| Runtime libraries | Do they match the chosen package? |
| Python environment | Is it the intended interpreter? |
| Container | Can it access the GPU? |
| Model | Is its architecture and format supported? |

Keep observations from each layer distinct. Replacing a container cannot fix a missing host device, and importing a library does not demonstrate a completed model request.

Avoid installing multiple conflicting variants into an owner's working environment. Preserve the original state and use a separate reproducible setup where practical. An unexplained successful result after many changes is difficult to hand over.

## Choose one installation route

The Docker documentation describes official images and their requirements. Containers help identify dependencies but still require a compatible host. Record the exact image reference and launch arguments.

For a Python route, save interpreter and package versions along with the installation source. If a documented build is needed for particular hardware, treat that as a defined path rather than mixing arbitrary commands from unrelated versions.

## Verify the model independently

Begin with a small supported artifact and bounded context. Record loading, readiness, API model identity and one successful request. Then repeat after a normal restart. This tests more than package import without confusing installation with a sustained capacity benchmark.

If the chain fails, preserve the last successful stage and original error before changing settings. A memory failure and an unsupported architecture require different next actions.

That package helps identify remaining preparation work and assess an appropriate pilot without requiring a new GPU purchase before the compatibility question is understood.

Continue with [First Verifiable vLLM Launch in Docker on NVIDIA GPUs](/journal/vllm-docker-first-launch/) and [vLLM Quantization: Match the Weights, Format and GPU](/journal/vllm-quantization-launch/).

## Official references

- [vLLM — gpu](https://docs.vllm.ai/en/stable/getting_started/installation/gpu/)
- [vLLM — docker](https://docs.vllm.ai/en/stable/deployment/docker/)