vLLM is an open-source inference engine designed to make large language model (LLM) serving fast, memory-efficient, and accessible. By introducing novel techniques—most notably PagedAttention for managing attention caches—vLLM achieves state-of-the-art throughput while keeping GPU memory usage in check.
vLLM has quickly evolved into a community-driven project Unlike monolithic inference solutions that can exhaust GPU RAM as context windows grow, vLLM decouples memory management from execution by paging in only the required portions of its key-value cache—enabling longer context lengths without out-of-memory errors.
With seamless integration into -- the Hugging Face ecosystem (vLLM can load any Transformer-style model available on Hugging Face—ranging from Llama and Mixtral to embedding models), OpenAI-compatible APIs (makes migrating existing applications to vLLM seamless and backward-compatible with established tooling) , offers tensor parallelism and pipeline parallelism, allowing models to be split across multiple GPUs or nodes, and support for NVIDIA, AMD, Intel, and AWS Neuron hardware -- vLLM allows developers and organizations of all sizes to deploy powerful generative AI services without prohibitive infrastructure costs.
whats in vLLM that makes it special ?
vLLM achieves impressive efficiency and speed through several clever techniques. It uses something called PagedAttention, which is inspired by how computer operating systems manage memory; it breaks down the attention memory into smaller, more manageable pieces and only loads what's actively needed, keeping things running smoothly even with longer prompts.
To handle multiple users accessing the model at once, vLLM groups together requests that start the same way, processing them together to save computation time and significantly increase how much data the system can handle.
Furthermore, by using specialized execution pathways built for NVIDIA or AMD graphics cards, it drastically reduces processing delays and makes sure the GPU is used to its fullest potential, allowing it to generate thousands of tokens every second. The system also speeds up the generation process by predicting upcoming tokens ahead of time and breaking down the initial text processing into smaller, quicker steps, which noticeably cuts down on wait times. Finally, to make the models less demanding on hardware, vLLM supports various methods for reducing the precision of the model weights (quantization), including advanced techniques like GPTQ and AWQ, and integrates with highly optimized code like FlashAttention and FlashInfer to make attention calculations much faster and reduce how much data needs to move around.
By combining OS-inspired paging, continuous batching, and hardware-specific optimizations, vLLM redefines what it means to serve LLMs at scale—delivering unmatched throughput on modest infrastructure.
https://github.com/vllm-project/vllm?tab=readme-ov-file#about
