vLLM is a cutting-edge library designed for efficient inference and serving of large language models (LLMs). Originally developed at UC Berkeley's Sky Computing Lab, it has grown into a community-driven project with contributions from both academia and industry.
Key Features:
- High Performance: Achieves state-of-the-art serving throughput with optimized CUDA/HIP graph execution, continuous batching, and efficient memory management via PagedAttention.
- Flexibility: Supports a wide range of models, including Transformer-based LLMs, Mixture-of-Experts models, embedding models, and multi-modal LLMs.
- Quantization & Optimization: Offers various quantization techniques (GPTQ, AWQ, INT4, INT8, FP8) and integrates with FlashAttention and FlashInfer for enhanced speed.
- Distributed Inference: Features tensor parallelism and pipeline parallelism for scalable deployment across multiple GPUs and CPUs.
- User-Friendly: Provides an OpenAI-compatible API server, streaming outputs, and seamless integration with Hugging Face models.
Community & Support:
- vLLM is backed by major organizations like a16z, NVIDIA, and AWS, which provide compute resources and funding.
- The project includes extensive documentation, a developer Slack channel, and a user forum for collaboration and support.
Performance Benchmarks:
- vLLM outperforms other LLM serving engines like TensorRT-LLM and LMDeploy in terms of speed and efficiency, as demonstrated in detailed benchmarks available in the project's blog.
Getting Started:
- Install vLLM via pip (
pip install vllm) or from source. - Explore the documentation for setup and usage guides.
vLLM is ideal for researchers and developers looking to deploy LLMs efficiently, with support for a broad spectrum of hardware, including NVIDIA and AMD GPUs, Intel CPUs, and AWS Neuron.