vLLM

vLLM: Complete Review

vLLM has emerged as one of the most widely adopted open source inference engines for large language models. Developed by UC Berkeley's Sky Computing Lab with over 2000 contributors, it combines academic research with practical engineering to achieve state-of-the-art serving throughput.

PagedAttention: The Key Innovation

vLLM's signature contribution is PagedAttention, a memory management algorithm for attention key and value tensors. Traditional approaches allocate contiguous memory blocks that waste space due to fragmentation. PagedAttention pages memory like an operating system pages virtual memory, dramatically reducing waste and allowing more requests to be served simultaneously. This innovation is why vLLM achieves higher throughput than standard implementations.

Performance and Features

Beyond PagedAttention, vLLM includes continuous batching for processing multiple requests concurrently, chunked prefill for handling long inputs efficiently, and prefix caching for reusing computed context across requests. It supports multiple quantization methods including FP8, INT8, INT4, GPTQ, AWQ, and GGUF for trading quality against efficiency. Speculative decoding with methods like EAGLE and DFlash reduces latency for interactive applications.

Model and Hardware Support

vLLM supports over 200 model architectures on Hugging Face, covering decoder-only LLMs, Mixture-of-Experts models, hybrid attention/state-space models, and multi-modal models. Its hardware support spans NVIDIA, AMD, Intel GPUs, Google TPUs, and Apple Silicon. This breadth means organizations can serve virtually any open source model on their preferred hardware.

Strengths

Developed by UC Berkeley Sky Computing Lab with 2000+ contributors

Apache-2.0 open source license

State-of-the-art serving throughput via PagedAttention

Wide quantization support for efficient inference

Supports 200+ model architectures on Hugging Face

OpenAI-compatible API for easy integration

Multi-hardware support (NVIDIA, AMD, Intel, TPUs, Apple Silicon)

Active research backing and rapid development

Limitations

Requires technical expertise to deploy

No managed service (self-hosted only)

Requires powerful hardware for large models

Rapid development can introduce instability

Documentation lags behind feature development

Verdict

vLLM is the best choice for organizations that need high-throughput LLM serving with strong research backing. Its PagedAttention innovation and broad model support make it the most capable open source inference engine available. For teams with technical expertise who want to maximize throughput while minimizing hardware costs, vLLM is the clear leader.