vllm: what it is, what problem it solves & why it's gaining traction

What it solves

vLLM is designed to make Large Language Model (LLM) inference and serving fast, easy, and cost-effective. It addresses the bottleneck of memory management and throughput when deploying LLMs at scale.

How it works

vLLM uses a technique called PagedAttention, which efficiently manages the attention key and value memory. It also employs continuous batching of incoming requests, chunked prefill, and prefix caching to maximize throughput. For performance, it utilizes optimized attention kernels (like FlashAttention) and supports various quantization methods (such as FP8, INT8, and AWQ) to reduce memory footprint and increase speed.

Who it’s for

It is for developers and researchers who need to deploy LLMs with high throughput and low latency, supporting a wide range of hardware including NVIDIA and AMD GPUs, as well as various CPUs and specialized NPUs/TPUs.

Highlights

• High Throughput: State-of-the-art serving throughput with continuous batching and optimized kernels.
• Broad Model Support: Seamlessly supports over 200 Hugging Face model architectures, including decoder-only, MoE, and multi-modal models.
• Flexible API: Provides an OpenAI-compatible API server, as well as support for Anthropic Messages API and gRPC.
• Hardware Agnostic: Works across NVIDIA GPUs, AMD GPUs, x86/ARM/PowerPC CPUs, and plugins for Google TPUs, Intel Gaudi, and others.
• Advanced Decoding: Supports speculative decoding, parallel sampling, and beam search.