vLLM

Definition

vLLM is an open-source library for high-throughput, memory-efficient serving of large language models. It is best known for introducing PagedAttention, a memory management technique for the attention key-value cache that lets a serving system handle many more concurrent requests on the same GPU hardware than earlier inference implementations, and it now underpins a large share of self-hosted LLM deployments in the open-source community.

Overview

Serving large language models in production is bottlenecked less by raw compute than by GPU memory, because each in-flight request needs its own key-value cache that grows with the length of the conversation. Naive implementations allocate this cache contiguously and pessimistically, wasting large amounts of memory to fragmentation and over-provisioning, which limits how many requests a single GPU can serve at once. vLLM was built specifically to address this memory bottleneck rather than treat it as an unavoidable cost of serving transformers. Its core mechanism, PagedAttention, borrows an idea from operating system virtual memory: instead of storing each request's key-value cache in one contiguous block, it splits the cache into fixed-size blocks that can be allocated non-contiguously and shared between requests, for example when multiple requests share a common prompt prefix. This dramatically reduces memory waste and allows vLLM to pack more concurrent sequences into the same GPU memory, combined with continuous batching that adds and removes requests from a running batch dynamically rather than waiting for a fixed batch to complete together, keeping GPU utilization high even under uneven request patterns. vLLM sits among a set of specialized inference-serving engines that emerged specifically to run open-weight and open-source LLMs efficiently, distinct from general-purpose inference platforms like Triton or ONNX Runtime that support many model types, and distinct from hosted API services that abstract the serving layer away entirely. It focuses narrowly and deeply on transformer-based language model serving rather than trying to be a general model-serving framework, which is part of why it iterates quickly on LLM-specific optimizations. In practice, vLLM is used to stand up an OpenAI-compatible API server for open-weight models such as those in the Llama or Mistral families, to power research and production inference pipelines that need high request throughput, and as a backend integrated into higher-level serving and orchestration tools that need an efficient LLM inference engine underneath them, including retrieval-augmented generation systems and agent frameworks. Limitations include a primary focus on NVIDIA GPU hardware and CUDA, meaning support for other accelerators has historically lagged, and, like any actively developed inference engine, keeping pace with the newest model architectures can require waiting for or contributing support. Teams needing multi-framework support across non-transformer model types, or wanting the vendor-backed support model of a commercial platform, sometimes look to alternatives like Triton Inference Server instead, trading vLLM's LLM-specific performance edge for broader compatibility guarantees and enterprise support contracts.

Key Features

• Implements PagedAttention for efficient key-value cache memory management
• Supports continuous batching to maximize GPU utilization across requests
• Exposes an OpenAI-compatible API server for easy integration
• Focuses specifically on transformer-based large language model serving
• Shares memory blocks across requests with common prompt prefixes
• Achieves higher request throughput than naive serving implementations
• Is open source and widely adopted for self-hosted LLM inference

Use Cases

Alternatives

Frequently Asked Questions

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