Application overview
vLLM provides APIs for offline inference and online serving. Supported Python versions, accelerator backends, driver requirements, and installation commands change across releases; check the current GPU-specific installation guide.
Typical use cases
LLM inference and model serving, subject to hardware and software compatibility.
HPC compatibility
Compatibility details shown here reflect administrator-maintained project information. Review the upstream release documentation against your exact operating system, drivers, accelerator, MPI stack, and cluster policy before deployment.
Current quick start documents a Linux NVIDIA GPU installation; other backends have separate release-specific instructions.
Installation instructions
Commands are project-specific notes. Confirm prerequisites and linked upstream instructions for your own release and system. Do not run administrative commands without review.
Linux NVIDIA quick start
Prerequisites: uv installed; Python version and driver compatible with current vLLM docs
uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install vllm --torch-backend=autoCreates an isolated Python environment and installs vLLM using the current documented automatic PyTorch backend selection.
Verify: vllm --help
Review: GPU and driver compatibility is version-specific. Installation may fetch large packages and compiled kernels. Check the current hardware-specific guide first.
Documentation for these steps ↗Upstream references
- Project homepage ↗
- Source repository ↗ Link status: Verified
- Documentation ↗
- Download / package page ↗
Link details last reviewed 2026-10-12 by MCR Tech. Project versions, licenses, hardware behavior, and hosted model access can change; follow current upstream terms.
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