Meet Vllm For Faster More Efficient Llm Inference And Serving
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: | 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: 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. <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. 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. vLLM is a fast and easy-to-use library for LLM inference and serving. Initially developed at UC Berkeley’s Sky Computing Lab, vLLM has evolved into a community-driven project with contributions from both academia and industry.
vLLM’s innovative algorithms reduce GPU usage by 24 times compared to traditional methods (Huggingface Transformers) by shortening the duration with OS virtual memory design. As a result, vLLM has become very popular due to its high efficiency and low resource usage. To generate meaningful output, the model must understand the importance of each token in the context and its relationship with other tokens. In a typical transformers model, this contextualization is done as follows: The meaning of the current token is found with the Query, and its relationship with other sentences is investigated. The compatibility of other tokens with this token is stored in the Key parameter. The model calculates a similarity score by comparing each query with the keys.
These scores are combined with the Value, which represents the content of the token. For example, consider the following sentence: “It is a hot summer day, we should bring …” | Documentation | Blog | Paper | Discord | Twitter/X | Developer Slack | vLLM is a fast and easy-to-use library for LLM inference and serving. Performance benchmark: We include a performance benchmark at the end of our blog post.
It compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, SGLang and LMDeploy). The implementation is under nightly-benchmarks folder and you can reproduce this benchmark using our one-click runnable script. vLLM seamlessly supports most popular open-source models on HuggingFace, including: Find the full list of supported models here.
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Have You Ever Wondered How AI-powered Applications Like Chatbots, Code
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 ...
As Detailed In Our VLLM Introductory Article, Serving An LLM
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 efficie...
Let’s Briefly Touch On The Techniques Used By VLLM In
Let’s briefly touch on the techniques used by vLLM in order to improve performance and efficiently utilize GPU resources: | 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 event...
VLLM Seamlessly Supports Most Popular Open-source Models On HuggingFace, Including:
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 ...
Later Posts Will Dive Into Specific Subsystems. This Post Is
Later posts will dive into specific subsystems. This post is structured into five parts: 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...
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