vLLM
High-throughput and memory-efficient inference engine for LLMs with PagedAttention technology
Open SourceOpen source (Apache 2.0), free to use APIOpen Source api
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vLLM is an open-source library for fast LLM inference and serving. Its key innovation, PagedAttention, efficiently manages attention key-value memory, achieving near-zero waste. vLLM supports continuous batching, tensor parallelism, and is compatible with HuggingFace models. It's the backbone of many LLM serving deployments, offering OpenAI-compatible API endpoints.
Key Features
- PagedAttention memory management
- Continuous batching
- Tensor parallelism
- OpenAI-compatible API
- LoRA adapter support
- Quantization support
- Multi-GPU inference
Pros
- State-of-the-art throughput
- Easy to deploy
- OpenAI-compatible API
- Active development
Cons
- Requires GPU infrastructure
- Limited model architecture support vs TGI
- Configuration tuning needed for optimal performance
Tags
llm-inferenceopen-sourcehigh-throughputmodel-servinggpu
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