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LLMs promise to fundamentally change how we use AI across all industries. However, actually serving these models is challenging and can be surprisingly slow even on expensive hardware. Today we are excited to introduce vLLM, an open-source library for fast LLM inference and serving. vLLM utilizes PagedAttention, our new attention algorithm that effectively manages attention keys and values. vLLM equipped with PagedAttention redefines the new state of the art in LLM serving: it delivers up to 24x higher throughput than HuggingFace Transformers, without requiring any model architecture changes.
vLLM has been developed at UC Berkeley and deployed at Chatbot Arena and Vicuna Demo for the past two months. It is the core technology that makes LLM serving affordable even for a small research team like LMSYS with limited compute resources. Try out vLLM now with a single command at our GitHub repository.
Beyond State-of-the-art Performance
We compare the throughput of vLLM with HuggingFace Transformers (HF), the most popular LLM library and HuggingFace Text Generation Inference (TGI), the previous state of the art. We evaluate in two settings: LLaMA-7B on an NVIDIA A10G GPU and LLaMA-13B on an NVIDIA A100 GPU (40GB). We sample the requests’ input/output lengths from the ShareGPT dataset. In our experiments, vLLM achieves up to 24x higher throughput compared to HF and up to 3.5x higher throughput than TGI.
Serving throughput when each request asks for one output completion. vLLM achieves 14x - 24x higher throughput than HF and 2.2x - 2.5x higher throughput than TGI.
Serving throughput when each request asks for three parallel output completions. vLLM achieves 8.5x - 15x higher throughput than HF and 3.3x - 3.5x higher throughput than TGI.
The Secret Sauce: PagedAttention
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