Vllm High Performance Llm Inference And Serving

| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack | Join us at the PyTorch Conference, October 22-23 and Ray Summit, November 3-5 in San Francisco for our latest updates on vLLM and to meet the vLLM team! Register now for the largest vLLM community events of the year! vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM seamlessly supports most popular open-source models on HuggingFace, including:

Originally posted on Aleksa Gordic’s website. In this post, I’ll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I’ll be doing a breakdown of how vLLM [1] works. This post is the first in a series. It starts broad and then layers in detail (following an inverse-pyramid approach) so you can form an accurate high-level mental model of the complete system without drowning in minutiae. Later posts will dive into specific subsystems.

This post is structured into five parts: Have you ever wondered how AI-powered applications like chatbots, code assistants and more respond so quickly? Or perhaps you’ve experienced the frustration of waiting for a large language model (LLM) to generate a response, wondering what’s taking so long. Well, behind the scenes, there’s an open source project aimed at making inference, or responses from models, more efficient. vLLM, originally developed at UC Berkeley, is specifically designed to address the speed and memory challenges that come with running large AI models. It supports quantization, tool calling and a smorgasbord of popular LLM architectures (Llama, Mistral, Granite, DeepSeek—you name it).

Let’s learn the innovations behind the project, why over 40k developers have starred the project on GitHub and how to get started with vLLM today! As detailed in our vLLM introductory article, serving an LLM requires an incredible amount of calculations to be performed to generate each word of their response. This is unlike other traditional workloads, and can be often expensive, slow and memory intensive. For those wanting to run LLMs in production, this includes challenges such as: With the need for LLM serving to be affordable and efficient, vLLM arose from a research paper called, “Efficient Memory Management for Large Language Model Serving with Paged Attention," from September of 2023, which... The results?

Up to 24x throughput improvements compared to similar systems such as HuggingFace Transformers and Text Generation Inference (TGI), with much less KV cache waste. Let’s briefly touch on the techniques used by vLLM in order to improve performance and efficiently utilize GPU resources: arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community?

Learn more about arXivLabs. <img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%> | Documentation | Blog | Paper | Discord | Twitter/X | Developer Slack | vLLM x Snowflake Meetup (Wednesday, November 13th, 5:30-8PM PT) at Snowflake HQ, San Mateo We are excited to announce the last in-person vLLM meetup of the year! Join the vLLM developers and engineers from Snowflake AI Research to chat about the latest LLM inference optimizations and your 2025 vLLM wishlist!

Register here and be a part of the event! vLLM is a fast and easy-to-use library for LLM inference and serving. Serve high throughput inference with vLLM Evaluate accuracy with LM Evaluation Harness vLLM is an open-source, high-throughput inference and serving engine for large language models (LLMs). It’s designed to make LLM inference faster, more memory-efficient, and scalable, particularly during the prefill (context processing) and decode (token generation) phases of inference.

vLLM supports Hugging Face Transformer models out-of-the-box and scales seamlessly from single-prompt testing to production batch inference. In this Learning Path, you’ll build a CPU-optimized version of vLLM targeting the Arm64 architecture, integrated with oneDNN and the Arm Compute Library (ACL). This build enables high-performance LLM inference on Arm servers, leveraging specialized Arm math libraries and kernel optimizations. After compiling, you’ll validate your build by running a local chat example to confirm functionality and measure baseline inference speed. vLLM has been a successful LLM inference and serving engine that excels at providing innovative features to users and developers. Earlier this year, the vLLM community introduced a major upgrade of its core engine and architecture to vLLM V1 (V1), which enhances the flexibility and scalability of the engine while retaining its core features.

For simplicity, we’ll refer to vLLM V0 as “v0” and vLLM V1 as “V1” throughout this post. To align with the vLLM community’s continuous innovation, the AMD ROCm™ software team and open-source ROCm developers have enabled the fully optimized vLLM V1 engine on AMD GPUs. In this blog, we explore key improvements introduced in vLLM V1 for AMD GPUs, as well as the main benefits users can expect from migrating to this new version. The new V1 engine has multiple improvements that include: a. The asynchronous process of the V1’s scheduler effectively separates CPU-intensive operations, such as token/de-tokenization and image preprocessing, from the GPU-intensive model inference process in a non-blocking manner as shown in Figure 1.

b. This feature enables higher compute utilization, especially for multimodal LLM performance, which heavily relies on the CPU for preprocessing. vLLM is a cutting-edge library for large language model inference and serving, offering unparalleled speed and efficiency through its innovative PagedAttention algorithm and seamless integration with popular AI frameworks. Achieve state-of-the-art performance in LLM serving with PagedAttention technology by efficiently managing attention keys and values, optimizing memory usage for faster inference and handling multiple requests simultaneously with ease. Seamlessly work with popular AI frameworks and models through native support for Hugging Face models, an OpenAI-compatible API server and easy integration with existing AI pipelines. Effortlessly scale your LLM applications to meet growing demands with Kubernetes-ready deployment, dynamic resource allocation and support for distributed inference across multiple nodes.

Simplify LLM deployment and management for developers of all skill levels with an intuitive API for quick implementation, comprehensive documentation and examples and an optional Gradio interface for easy interaction. In this post, I'll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I'll be doing a breakdown of how vLLM [1] works. This post is the first in a series. It starts broad and then layers in detail (following an inverse-pyramid approach) so you can form an accurate high-level mental model of the complete system without drowning in minutiae. Later posts will dive into specific subsystems.

This post is structured into five parts: The LLM engine is the fundamental building block of vLLM. On its own, it already enables high-throughput inference - but only in an offline setting. You can't serve it to customers over the web yet.

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