vLLM

大模型“魔法”加速器：深入浅出vLLM

想象一下，你是一家异常繁忙的餐厅老板，你的主厨（也就是当下最热门的“大语言模型”，简称LLM）拥有惊人的烹饪技艺，能根据顾客的各种需求（文本输入）变出美味佳肴（生成回答）。然而，这家餐厅面临着一个大问题：顾客点餐的速度越来越快，而主厨虽然手艺精湛，但每次只能一道菜一道菜地做，厨房的效率低下，导致顾客等待时间超长，而且食材（计算资源）和厨房空间（内存）的浪费非常严重。

随着人工智能的飞速发展，大型语言模型（LLM）已经成为我们生活中不可或缺的一部分，它们能写诗、编程、翻译甚至聊天，就像那位无所不能的主厨。然而，这些庞大模型的推理过程（即“做菜”过程）却是一个巨大的挑战，它们对计算资源的需求极高，速度慢，成本也高。为了解决这些问题，一个明星级的“厨房管理系统”应运而生，它就是我们今天要介绍的主角——vLLM。

什么是vLLM？

vLLM全称是“Virtual Large Language Model”，它不是一个具体的语言模型，而是一个专门为大语言模型推理加速而设计的开源高性能推理引擎。你可以把它理解为一套极其智能的厨房管理系统，它的任务是确保主厨（LLM）在处理海量订单时，能以最快、最有效率的方式工作，最大化利用厨房（GPU）的每一个角落，同时尽量减少食材（内存）的浪费。

大模型推理的困境：为何需要vLLM？

为什么说大模型的推理很困难呢？让我们继续用餐厅来打比方：

  1. 计算量巨大，每一道菜都超级复杂：LLM的每一次回答，都需要进行海量的计算，就像主厨每次制作的都是一道道需要精雕细琢的米其林大餐，耗时耗力。
  2. “记忆”负担沉重（KV Cache）：主厨在烹饪每道菜时，为了确保味道连贯，会把之前用到的所有复杂配料和烹饪心得（大模型中的“注意力键”Key 和“注意力值”Value，简称KV Cache）都堆在工作台上。这些“记忆”会随着菜品复杂度的增加而不断累积，占据大量宝贵的厨房工作台空间（显存）。传统方式下，即使菜品很多，每道菜的记忆区域是固定的，导致大量空闲但被占用的空间，造成严重的内存碎片化和浪费。
  3. 效率低下，顾客等待时间长（低吞吐量）：传统餐厅通常采用“一道菜做完再做下一道”的方式。如果同时有几十上百位顾客点餐，主厨必须顺序完成，这导致很多顾客需要长时间等待，也就是模型的“吞吐量”很低。

这些困境共同导致了大模型推理的速度瓶颈、高延迟和高昂的运营成本。

vLLM的魔法：两大核心技术

vLLM的厉害之处在于它引入了两项革命性的技术，从根本上解决了上述难题：PagedAttention（分页注意力机制）和Continuous Batching（连续批处理）。正是凭借这两项创新，vLLM能够将LLM的吞吐量提升高达24倍，同时大幅降低延迟和硬件成本。

1. PagedAttention（分页注意力机制）：智能的“记忆”管理大师

为了解决“记忆”负担沉重的问题，vLLM提出了PagedAttention机制。这就像是给主厨配备了一个极其智能的配料管理系统：

  • 传统方式的浪费：以前，主厨每开始一道新菜，就会划定一块固定大小的工作台区域来放置这道菜的配料和心得。但菜品的实际复杂度和所需配料量是不同的，有时菜很简单，这块区域大部分都空着；有时一放就是一堆，但不管用不用，这块区域都被“预定”了，其他菜也不能用。这导致了厨房空间巨大的浪费。

  • PagedAttention的创新：PagedAttention机制的灵感来源于操作系统中的虚拟内存管理技术。它不再为每道菜预留固定大小的空间，而是将每道菜的“记忆”（KV Cache）切分成许多小份的“记忆块”（Page）。当主厨需要某个“记忆块”时，系统会动态地从一个公共的“记忆库”中分配一块物理空间给它。这些物理空间不一定是连续的，就像图书馆里的书可能分开放置，但目录（Block Table）会准确记录每一页的位置。

    更妙的是，如果多道菜有共同的、重复的“记忆”（例如，所有顾客都点了同一道开胃菜，或者某个菜的制作初期步骤是相同的），PagedAttention可以让它们共享这些“记忆块”。只有当它们开始产生不同的“记忆”（菜品产生了独有的变化）时，系统才会复制并为新的部分分配独立的记忆块（写时复制，Copy-on-Write机制）。

    效果：通过这种方式，PagedAttention大大减少了KV Cache的内存浪费，显存利用率接近100%，传统LLM推理引擎的内存浪费可能高于96%，而vLLM能减少到不足4%。这意味着厨房工作台不再堆满无用配料，主厨有更多空间同时处理更多订单。

2. Continuous Batching（连续批处理）：流水线式的订单处理专家

为了解决效率低下的问题，vLLM引入了Continuous Batching技术。这好比餐厅引入了一套智能化、流水线式的订单处理系统：

  • 传统批处理的不足：以前的批处理模式是“静态批处理”，就像餐厅攒够了一批订单（比如10个披萨），主厨一起制作这10个披萨，等所有披萨都烤完上桌了，才开始处理下一批订单。如果某个披萨需要额外加料，耗时很长，后面所有顾客都得等着。

  • Continuous Batching的创新：Continuous Batching就像是持续流动的订单处理。系统会动态地将正在进行中的（尚未完成的）和新来的（刚刚点餐的）顾客订单巧妙地组合在一起，并以最快的速度将它们送进主厨的“制作流水线”。一旦有订单完成或有新的GPU资源空闲下来，系统会立即将新的或等待中的订单补充进去，而不是等到一个批次全部完成。它会持续地将可用请求分批次送入LLM，只要GPU有空闲，就绝不让它停下来等待。

    效果：Continuous Batching极大地提高了GPU的利用率，使得大模型能够不间断地处理请求，就像一个智能的交通指挥系统，时刻保持道路畅通。这使得vLLM能够实现比传统方案高出数倍甚至数十倍的吞吐量，同时显著降低用户请求的响应延迟。

vLLM带来的改变

vLLM的出现，为大模型领域带来了革命性的影响：

  • 性能飞跃：根据某些基准测试，vLLM的吞吐量比Hugging Face Transformers（一个常用的LLM开源库）高出24倍。其最新版本更将吞吐量提高了2.7倍，延迟减少了5倍。这意味着同样的时间和资源，可以处理更多的请求，响应速度也更快。
  • 成本大幅降低：更高效的资源利用意味着处理LLM所需的GPU数量更少。有案例显示，使用vLLM后，处理相同流量所需的GPU数量减少了50%。这对于企业和开发者来说，无疑是巨大的利好。
  • 更广泛的兼容性和开放性：vLLM不仅兼容NVIDIA GPU，还在积极扩展对AMD GPU、Intel GPU、AWS Neuron、Google TPU等多种硬件的支持。它支持包括LLaMA、GPT-2在内的多种流行模型架构，并且能够轻松与Langchain等框架集成。作为一个开源项目，vLLM促进了社区的创新和发展。
  • 简单易用：vLLM提供了与OpenAI API兼容的服务器接口，使得开发者可以无缝集成到现有应用中，无需对模型代码进行修改即可部署。

最新进展与展望

vLLM项目持续活跃并迅速发展。2025年1月，vLLM发布了V1 Alpha版本，这是一个重要的架构升级，带来了1.7倍的速度提升，并增加了对多模态的支持。此外，vLLM还在不断优化其量化支持（例如bitsandbytes, QQQ, FP8 KV缓存），并支持更广泛的模型架构。

可以说，vLLM正在成为大模型推理领域的行业标准和驱动力。

总结

vLLM就像是大模型餐厅里那位无声的英雄——一套高效而智能的厨房管理系统。它通过PagedAttention巧妙地管理“记忆空间”，杜绝浪费；再通过Continuous Batching流水线式地处理订单，让每一份计算资源都发挥最大价值。正是这两项“魔法”，让大语言模型能够更快、更便宜、更高效地服务于我们，将先进的AI技术普惠到更广泛的应用场景中。未来，有了vLLM这样的技术，我们可以期待大模型在各个领域发挥更大的潜力，真正走进千家万户。

vLLM: The “Magic” Accelerator for Large Models

Imagine you are the owner of an extremely busy restaurant. Your head chef (the currently hottest “Large Language Model”, LLM for short) possesses amazing culinary skills and can conjure up delicious dishes (generate answers) based on various customer requests (text inputs). However, this restaurant faces a big problem: customers are ordering faster and faster, while the chef, despite their exquisite skills, can only cook one dish at a time. The kitchen efficiency is low, causing customers to wait for a very long time, and the waste of ingredients (computing resources) and kitchen space (memory) is very serious.

With the rapid development of Artificial Intelligence, Large Language Models (LLMs) have become an indispensable part of our lives. They can write poetry, code, translate, and even chat, just like that omnipotent chef. However, the inference process of these huge models (the “cooking” process) is a huge challenge. They have extremely high demands on computing resources, are slow, and expensive. To solve these problems, a star-level “kitchen management system” came into being, which is the protagonist we are introducing today—vLLM.

What is vLLM?

vLLM stands for “Virtual Large Language Model”. It is not a specific language model, but an open-source high-performance inference engine designed specifically to accelerate the inference of large language models. You can understand it as an extremely intelligent kitchen management system. Its task is to ensure that the chef (LLM) works in the fastest and most efficient way when processing massive orders, maximizing the use of every corner of the kitchen (GPU), while minimizing the waste of ingredients (memory).

The Dilemma of Large Model Inference: Why Do We Need vLLM?

Why is the inference of large models so difficult? Let’s continue to use the restaurant analogy:

  1. Huge Calculation Volume, Every Dish is Super Complex: Every answer from an LLM requires massive calculations, just like every dish made by the chef is a meticulously crafted Michelin meal, consuming time and effort.
  2. Heavy “Memory” Burden (KV Cache): When the chef cooks each dish, to ensure consistent taste, they will pile all the complex ingredients and cooking tips used before (the “Attention Key” and “Attention Value” in the large model, referred to as KV Cache) on the workbench. These “memories” will accumulate as the complexity of the dish increases, occupying a large amount of valuable kitchen workbench space (GPU memory). In the traditional way, even if there are many dishes, the memory area for each dish is fixed, resulting in a lot of idle but occupied space, causing serious memory fragmentation and waste.
  3. Low Efficiency, Long Wait Time for Customers (Low Throughput): Traditional restaurants usually adopt the “finish one dish before doing the next” method. If dozens or hundreds of customers order at the same time, the chef must complete them sequentially. This causes many customers to wait for a long time, meaning the model’s “throughput” is very low.

These dilemmas collectively lead to speed bottlenecks, high latency, and high operating costs for large model inference.

The Magic of vLLM: Two Core Technologies

The greatness of vLLM lies in its introduction of two revolutionary technologies, which fundamentally solve the above problems: PagedAttention and Continuous Batching. It is precisely with these two innovations that vLLM can increase LLM throughput by up to 24 times, while significantly reducing latency and hardware costs.

1. PagedAttention: The Intelligent “Memory” Management Master

To solve the heavy “memory” burden problem, vLLM proposed the PagedAttention mechanism. This is like equipping the chef with an extremely intelligent ingredient management system:

  • Waste in Traditional Methods: Previously, every time the chef started a new dish, a fixed-size area of the workbench would be designated to place the ingredients and tips for this dish. But the actual complexity of dishes and the amount of ingredients required vary. Sometimes the dish is very simple, and most of this area is empty; sometimes it’s a pile, but regardless of whether it is used or not, this area is “reserved” and cannot be used by other dishes. This led to a huge waste of kitchen space.

  • Innovation of PagedAttention: The inspiration for the PagedAttention mechanism comes from the virtual memory management technology in operating systems. It no longer reserves a fixed-size space for each dish but divides the “memory” (KV Cache) of each dish into many small “memory blocks” (Pages). When the chef needs a certain “memory block”, the system will dynamically allocate a physical space from a public “memory pool” to it. These physical spaces do not need to be continuous, just like books in a library may be placed separately, but the catalog (Block Table) will accurately record the location of each page.

    Even better, if multiple dishes have common, repeated “memories” (for example, all customers ordered the same appetizer, or the initial steps of making a certain dish are the same), PagedAttention allows them to share these “memory blocks”. Only when they start to produce different “memories” (the dish produces unique changes) will the system copy and allocate independent memory blocks for the new part (Copy-on-Write mechanism).

    Effect: By this means, PagedAttention greatly reduces the memory waste of KV Cache. The GPU memory utilization rate approaches 100%. The memory waste of traditional LLM inference engines may be higher than 96%, while vLLM can reduce it to less than 4%. This means the kitchen workbench is no longer piled with useless ingredients, and the chef has more space to process more orders simultaneously.

2. Continuous Batching: Pipeline Order Processing Expert

To solve the problem of low efficiency, vLLM introduced Continuous Batching technology. This is like the restaurant introducing an intelligent, pipeline-style order processing system:

  • Shortcomings of Traditional Batching: The previous batching mode was “static batching”, like the restaurant accumulating a batch of orders (say, 10 pizzas), and the chef making these 10 pizzas together. Only when all pizzas are baked and served will the next batch of orders be processed. If one pizza requires extra toppings and takes a long time, all subsequent customers have to wait.

  • Innovation of Continuous Batching: Continuous Batching is like continuously flowing order processing. The system will dynamically combine the ongoing (unfinished) and new (just ordered) customer orders and send them into the chef’s “production pipeline” at the fastest speed. Once an order is completed or new GPU resources become free, the system will immediately supplement new or waiting orders, instead of waiting for a batch to be fully completed. It will continuously send available requests into the LLM in batches, and as long as the GPU is free, it will never let it stop and wait.

    Effect: Continuous Batching greatly improves the utilization of GPU, allowing the large model to process requests uninterruptedly, just like an intelligent traffic command system, keeping the road clear at all times. This enables vLLM to achieve throughput several times or even tens of times higher than traditional solutions, while significantly reducing the response latency of user requests.

Changes Brought by vLLM

The emergence of vLLM has brought revolutionary impacts to the large model field:

  • Performance Leap: According to some benchmarks, vLLM’s throughput is 24 times higher than Hugging Face Transformers (a commonly used LLM open-source library). Its latest version has increased throughput by 2.7 times and reduced latency by 5 times. This means that with the same time and resources, more requests can be processed, and the response speed is faster.
  • Significant Cost Reduction: More efficient resource utilization means fewer GPUs are needed to process LLMs. Cases show that after using vLLM, the number of GPUs required to process the same traffic was reduced by 50%. For enterprises and developers, this is undoubtedly a huge benefit.
  • Broader Compatibility and Openness: vLLM is not only compatible with NVIDIA GPUs but is also actively expanding support for various hardware such as AMD GPUs, Intel GPUs, AWS Neuron, Google TPUs, etc. It supports multiple popular model architectures including LLaMA and GPT-2, and can be easily integrated with frameworks like Langchain. As an open-source project, vLLM allows community innovation and development.
  • Simple and Easy to Use: vLLM provides a server interface compatible with OpenAI API, allowing developers to seamlessly integrate it into existing applications and deploy without modifying model codes.

Latest Progress and Outlook

The vLLM project continues to be active and develops rapidly. In January 2025, vLLM released the V1 Alpha version, which is a major architectural upgrade that brought a 1.7x speed increase and added support for multi-modality. In addition, vLLM is constantly optimizing its quantization support (such as bitsandbytes, QQQ, FP8 KV cache) and supporting a wider range of model architectures.

It can be said that vLLM is becoming the industry standard and driving force in the field of large model inference.

Summary

vLLM is like the unsung hero in the large model restaurant—an efficient and intelligent kitchen management system. It cleverly manages “memory space” through PagedAttention to eliminate waste; and processes orders in a pipeline style through Continuous Batching to maximize the value of every computing resource. It is precisely these two “magics” that allow large language models to serve us faster, cheaper, and more efficiently, bringing advanced AI technology to a wider range of application scenarios. In the future, with technologies like vLLM, we can expect large models to unleash greater potential in various fields and truly enter thousands of households.