Purpose
vLLM is a high-throughput, memory-efficient inference and serving engine for large language models (LLMs), with the goal of making high-performance LLM serving simpler, faster, and more cost-effective for everyone.
Main Capabilities
- High throughput and memory efficiency: Maximize throughput with PagedAttention, and ensure peak GPU utilization through advanced scheduling and continuous batching.
- Broad compatibility: A single engine can run any model on any hardware, supports the latest popular open-source models, and is optimized for production environments.
- Unified API: Provides a unified API across platforms, and includes an OpenAI-compatible plug-and-play API for instant integration.
- Cost-effectiveness: Greatly reduce inference costs by maximizing hardware efficiency, making high-performance LLMs more affordable and accessible.
How to Use
When installing, you need to choose your preferences and run the installation command. Stable represents the currently tested and supported version; if you need the latest build, you can use the Nightly version.
- Requires Python 3.10+, Python 3.12+ recommended.
- uv is recommended for faster, more reliable installation.
- For other platforms, please refer to docs.vllm.ai.
- Compatible with all CUDA 13.x versions (13.0 - 13.1).
Use Cases
Suitable for deploying a wide range of open-source LLMs on various hardware, requiring high-throughput, memory-efficient production-grade inference serving, and for scenarios where users want to obtain high-performance LLM serving at low cost.
Limitations and Notes
vLLM is a community project. The compute resources for development and testing are provided by supporting organizations, and it accepts donations through GitHub and OpenCollective to support development, maintenance, and adoption. Information such as the specific list of supported models, performance benchmark details, platform compatibility scope, and the latest version changes is not fully provided in the material; it is recommended to consult the official documentation and roadmap.
