Introduction to vLLM
vLLM is a robust and efficient tool designed for serving Large Language Models (LLMs). This project places a strong emphasis on ease of use, speed, and affordability, making it accessible for a wide range of users. Whether it's for developers looking to integrate LLMs into their applications or researchers aiming to optimize model performance, vLLM offers a suite of features and tools to aid in these endeavors.
Key Features and Performance
vLLM excels in several areas:
- State-of-the-Art Serving Throughput: It offers rapid serving capabilities, ensuring high efficiency for applications.
- PagedAttention: This feature facilitates efficient management of memory involved in processing LLMs, optimizing performance and reducing resource use.
- Continuous Batching: Incoming requests are batched seamlessly, enhancing the throughput of query processing.
- Advanced Model Execution: Utilizing technologies like CUDA/HIP graphs, vLLM improves execution speed.
- Support for Various Quantizations: Including GPTQ, AWQ, INT4, INT8, and FP8, which allow for more flexible and efficient model approximations.
- Optimized CUDA Kernels: Integration with FlashAttention and FlashInfer for accelerated computation.
- Speculative Decoding and Chunked Prefill: These techniques further enhance the speed and efficiency of model inference.
A performance benchmark is available which showcases how vLLM compares to other LLM serving engines like TensorRT-LLM, SGLang, and LMDeploy. This data is available for reproduction through a one-click script.
Flexibility and Ease of Use
vLLM is designed to be highly flexible, supporting:
- Seamless Integration: Compatible with popular Hugging Face models, facilitating easy adoption.
- High-Throughput Serving Algorithms: It supports various decoding approaches including parallel sampling and beam search.
- Distributed Inference: With support for tensor and pipeline parallelism.
- Streaming Outputs and API Compatibility: vLLM offers OpenAI-compatible APIs, which makes it easy to deploy within diverse infrastructures.
- Support for Multiple Hardware Architectures: Compatible with NVIDIA GPUs, AMD CPUs/GPUs, Intel CPUs/GPUs, PowerPC CPUs, TPU, and AWS Neuron.
- Advanced Features: Includes support for prefix caching and multi-lora configurations.
vLLM supports a wide range of models, from transformer-like LLMs such as Llama, to mixture-of-expert models like Mixtral, and multi-modal models like LLaVA, among others. A comprehensive list of supported models can be found in the documentation.
Getting Started
To start using vLLM, users can install it via pip or build it from source. Detailed installation instructions, a quickstart guide, and information on supported models are provided in the vLLM documentation.
Community and Contributions
vLLM welcomes contributions from the community. Instructions for getting involved are available in the CONRIBUTING file on their GitHub repository. The project also benefits from support by a variety of sponsors, including major industry names like a16z, NVIDIA, and AWS, among others. Additionally, there is an official fundraising effort through OpenCollective to support ongoing developments.
Participate and Engage
vLLM is not just a project but a community. For technical support, contributions, or just interacting with fellow users, vLLM provides several online platforms including GitHub, Discord, and a dedicated Slack channel for developers. For research-related use, the vLLM paper is available for citation to give credit where it is due.
The vLLM project stands as a compelling offering for those in need of efficient, high-performance LLM serving solutions.