vLLM

vLLM — Grokipedia Fact-checked by Grok 1 month ago vLLM Ara Eve Leo Sal 1x vLLM is an open-source library designed for high-throughput and memory-efficient inference and serving of large language models (LLMs). [1] It is one of the top-rated tools for LLM inference and serving, with over 72,000 GitHub stars as of early 2026. [1] Originally developed in the Sky Computing Lab at the University of California, Berkeley, it has evolved into a community-driven project that optimizes LLM deployment through innovative techniques like PagedAttention, which manages key-value caches using a paging mechanism inspired by virtual memory systems to reduce memory fragmentation and enable larger batch sizes. [2] [3] The library supports a range of deployment scenarios, including offline inference, OpenAI-compatible servers, and distributed setups such as tensor parallelism across multiple nodes using the NCCL backend on NVIDIA hardware or Ray as the distributed executor framework on AMD ROCm GPUs, with MoRI providing efficient KV cache handling in multi-node scenarios. [4] [5] Its core innovations, including PagedAttention and dynamic batching via continuous batching, allow vLLM to achieve significantly higher throughput compared to traditional serving frameworks, often by factors of 2x to 4x or more depending on the model and workload. [3] vLLM is implemented in Python with performance-critical components in C++ and CUDA, making it compatible with popular LLM frameworks like Hugging Face Transformers and supporting models from various architectures, including Llama , GPT , and Mistral. [1] As a production-ready engine, it emphasizes ease of use, with features like automatic quantization, automatic prefix caching (APC, enabled by default in recent versions), and integration with tools for monitoring and scaling, positioning it as a key tool in the ecosystem for efficient AI model serving. [6] [7] Introduction Overview vLLM is an open-source library designed for high-throughput and memory-efficient inference and serving of large language models (LLMs). [1] It serves as a fast and easy-to-use tool that optimizes the deployment of LLMs, addressing key challenges in speed and resource utilization for production environments . [4] Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project that supports efficient serving on various hardware setups. [2] The primary goal of vLLM is to enable scalable and performant deployment of LLMs, making it suitable for real-world applications requiring low-latency responses and high request volumes. [8] By focusing on innovations that minimize memory overhead and maximize inference speed, it helps bridge the gap between model training and practical serving. [1] A core innovation, such as PagedAttention for memory management , underpins its efficiency, though detailed mechanisms are explored elsewhere. [8] As an open-source initiative , vLLM benefits from ongoing contributions from the broader community, ensuring adaptability to emerging LLM architectures and hardware advancements. [4] This collaborative evolution has positioned it as a leading solution for LLM serving, emphasizing accessibility for developers and researchers alike. [2] History vLLM originated in the Sky Computing Lab at the University of California, Berkeley, where development began in early 2023 as part of efforts to optimize large language model serving. [2] The project was first publicly announced on June 20, 2023, via an official blog post introducing its core innovations for high-throughput inference. [9] This announcement coincided with the initial open-source release on GitHub under the bowang-lab organization, marking the library's debut as a research-driven tool for memory-efficient LLM serving. [10] The foundational work was detailed in the seminal paper "Efficient Memory Management for Large Language Model Serving with PagedAttention," published on arXiv on September 12, 2023, which proposed key techniques for reducing memory waste in KV caches during inference. [11] Following this, vLLM's GitHub repository transitioned to the vllm-project organization, evolving into a community-driven open-source initiative with contributions from academic institutions and industry partners, including integrations with frameworks like PyTorch. [1] Initial PyPI releases began in June 2023, with version 0.1.0 on June 19, 2023, and version 0.2.0 following on September 28, 2023, enabling broader adoption through easy installation. [6] Subsequent major updates have included bi-weekly patch releases, with significant milestones such as the v0.10.0 release in mid-2025, which incorporated advanced features like quantization support to further enhance performance on diverse hardware. [12] In May 2025, vLLM was officially hosted under the PyTorch Foundation, solidifying its role as a key component in the open-source AI ecosystem. [6] Technical Features Core Innovations vLLM's core innovations revolve around algorithmic and system-level optimizations designed to enhance the throughput and efficiency of large language model inference. At the heart of these advancements is PagedAttention, a novel memory management technique that draws inspiration from virtual memory paging in operating systems . This approach stores key-value (KV) caches in non-contiguous memory blocks , or " pages ," which significantly reduces memory fragmentation and enables more efficient allocation during dynamic request processing. By avoiding the need for contiguous memory reservations for variable-length sequences, PagedAttention allows vLLM to achieve up to 2.2 times higher throughput compared to existing systems on benchmarks like ShareGPT, while minimizing memory waste. [3] [1] Complementing PagedAttention is vLLM's implementation of continuous batching, which dynamically adjusts the batch size during inference to accommodate incoming requests without interrupting ongoing computations. Unlike traditional static batching that requires waiting for a full batch or terminating early sequences, continuous batching integrates new requests seamlessly into the current batch, preventing latency spikes and improving overall system utilization. This mechanism supports real-time serving scenarios by maintaining high throughput even under varying workloads, with reported improvements in request completion rates by up to 1.7 times over baselines. [3] [4] To accelerate attention computations, vLLM integrates optimized kernels such as FlashAttention, which fuses softmax and matrix multiplications to reduce memory accesses and enable faster processing on GPUs . FlashAttention's kernel-level optimizations are particularly effective for long sequences, contributing to vLLM's ability to handle high-throughput inference with reduced overhead. Additionally, vLLM supports speculative decoding, where a smaller draft model generates candidate tokens that are verified by the main model, potentially speeding up generation by up to 2 times in certain setups, and chunked prefill, which processes input prompts in smaller chunks to overlap computation and reduce initial latency. These features collectively enable vLLM to outperform frameworks like Hugging Face Transformers by orders of magnitude in serving speed. [1] [3] [4] vLLM also briefly supports quantization techniques such as GPTQ and AWQ to further optimize model weights for memory efficiency. [4] Automatic Prefix Caching Automatic Prefix Caching (APC) is enabled by default in recent versions and reuses KV-cache blocks for requests sharing identical prefixes, reducing prefill computation. To enable explicitly, set enable_prefix_caching=True in the engine args. Customize hashing with --prefix-caching-hash-algo (e.g., sha256_cbor for determinism). Benefits workloads like repeated long-document queries or multi-turn conversations with shared history, but provides no gain if prefixes differ or decoding dominates. [7] Supported Models and Hardware vLLM offers seamless integration with models from the Hugging Face Transformers library, enabling easy loading and serving of a wide range of large language models (LLMs). [13] It supports prominent architectures such as Llama, GPT, Mistral, GLM, DeepSeek, Qwen series, and others, with recent additions in vLLM 0.16.0 including GLM-OCR, Qwen3-ASR, and DeepSeek-OCR-2, allowing users to deploy these models with minimal configuration changes. [13] [14] This compatibility extends to third-party models hosted on Hugging Face, provided they adhere to standard transformer-based implementations. [13] To optimize memory usage, vLLM incorporates various quantization formats, including GPTQ, AWQ, INT4, INT8, and FP8, which reduce the precision of model weights and activations while maintaining inference accuracy. [15] These techniques are particularly useful for deploying larger models on resource-constrained environments, enabling higher throughput without proportional increases in hardware demands. [16] On the hardware front, vLLM is primarily optimized for NVIDIA GPUs across architectures like Volta (SM 7.0) , Turing (SM 7.5), Ampere (SM 8.0/8.6), Ada (SM 8.9), Hopper (SM 9.0), and Blackwell, with full support for quantization methods on these platforms. [17] It supports consumer GeForce RTX 50-series GPUs, including the RTX 5070 Ti, which are based on the Blackwell architecture and compatible via recent vLLM versions with CUDA 12.8 or later. [17] [18] Professional Blackwell GPUs such as the NVIDIA RTX PRO 6000 (including Server and Workstation Editions) are also compatible, with NVIDIA's vLLM Release 25.09 (and later versions) providing explicit functional support for the RTX PRO 6000 Blackwell Server Edition under CUDA 13.0. [19] Blackwell GPUs require a minimum of CUDA 12.8, with CUDA 13.0 or later recommended for optimal compatibility, particularly for systems like Grace-Blackwell. User reports and recent vLLM releases confirm successful inference on the RT