Principle:Vllm project Vllm Vllm Installation
| Knowledge Sources | |
|---|---|
| Domains | Machine Learning, Infrastructure, Package Management |
| Last Updated | 2026-02-08 13:00 GMT |
Overview
Package installation is the process of obtaining and configuring a software library and all its transitive dependencies into a local environment so that it can be imported and executed.
Description
Installing an LLM inference engine such as vLLM requires satisfying a complex dependency tree that spans Python packages (transformers, torch, numpy), compiled CUDA extensions, and system-level GPU drivers. The installation process must resolve version constraints across all of these layers to produce a working environment.
vLLM is distributed as a pre-built wheel on PyPI for supported platforms (Linux x86_64 with CUDA 12.x). The wheel bundles pre-compiled CUDA kernels for PagedAttention and other custom operators, which means the user does not need to compile from source in the common case. For platforms without a pre-built wheel, a source build is triggered automatically, requiring cmake, ninja, and a compatible CUDA toolkit.
Key dependency categories for vLLM include:
- CUDA runtime: vLLM ships wheels targeting CUDA 12.x. The host must have a compatible NVIDIA driver (driver version >= 525 is recommended).
- PyTorch: vLLM pins a specific torch version (e.g., torch == 2.9.1 in the build system) to ensure kernel compatibility.
- Hugging Face ecosystem: transformers and tokenizers are used for model loading and tokenization.
- Networking / serialization: Libraries such as ray (for distributed inference), msgspec, and zmq are used internally.
Usage
Install vLLM whenever you need high-throughput, batched LLM inference, either offline (batch processing) or online (serving via an API). A standard pip install is the recommended starting point for CUDA 12.x Linux environments. For custom hardware (AMD ROCm, AWS Neuron, TPU), consult the platform-specific installation pages in the vLLM documentation.
Theoretical Basis
Package management in Python relies on the PEP 517 / PEP 660 build system interface. vLLM uses setuptools as its build backend and setuptools-scm for dynamic versioning. The pyproject.toml file declares build-time requirements (cmake, ninja, torch, jinja2) and project metadata. Runtime dependencies are resolved dynamically by the setuptools configuration.
The installation proceeds in these conceptual stages:
- Dependency resolution: pip's resolver reads the package metadata and constructs a compatible set of versions for all transitive dependencies.
- Wheel download or build: If a pre-built wheel matches the platform, it is downloaded directly. Otherwise, a source distribution is fetched and built locally using the declared build system.
- Installation: The wheel contents are unpacked into the target site-packages directory, and entry points (e.g., the
vllmCLI) are registered.
Failure modes typically involve CUDA version mismatches, incompatible torch versions, or missing system libraries (e.g., libcudart). The vLLM documentation provides troubleshooting guidance for each of these scenarios.