vLLM - Fast and Easy LLM Inference

Official site: https://docs.vllm.ai/

Key Advantages

• Blazing Speed & Low Latency: PagedAttention plus continuous batching deliver up to 23× higher throughput and significantly reduced p50 latency.
• Incredibly Easy: One-line command to spin up an OpenAI-compatible high-throughput API server, seamlessly integrated with HuggingFace models.
• Comprehensive Quantization: Native support for GPTQ, AWQ, INT4/8, FP8 and more to save memory and boost speed.
• Extensive Hardware Support: NVIDIA GPUs, AMD CPUs/GPUs, Intel CPUs, Gaudi, IBM Power, TPUs, AWS Trainium & Inferentia.
• Advanced Features: Parallel sampling, beam search, speculative decoding, chunked prefill, prefix caching, multi-LoRA, streaming outputs.

Major Capabilities

• Distributed Inference: Tensor, pipeline, data and expert parallelism for effortless scaling across multiple nodes and GPUs.
• Enterprise-grade APIs: OpenAI-style RESTful endpoints including chat/completions, completions, etc.
• Community Ecosystem: Initiated by UC Berkeley, now a global, community-driven project with contributions from both academia and industry.

Explore the official docs, blog, paper or join a Meetup to experience the ultimate inference performance of vLLM!