Introduction

Deploying large language models (LLMs) in production often leads to a critical bottleneck: GPU memory fragmentation and inefficient KV cache management, which severely limits throughput and increases latency. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs that solves these problems using a novel memory management system. With over 86k GitHub stars, vLLM has become the industry standard for serving open-source models with maximum hardware utilization.

What Is vLLM?

vLLM is an open-source inference and serving engine that optimizes the execution of large language models for high-throughput production environments. Originally developed at the Sky Computing Lab at UC Berkeley, it is now a community-driven project under the PyTorch Foundation. It is written primarily in Python, CUDA, C++, and Rust, and is licensed under the Apache License 2.0.

The project’s core innovation is PagedAttention, a memory management technique inspired by virtual memory in operating systems. By treating the KV cache as non-contiguous blocks, vLLM eliminates external fragmentation and allows for significantly larger batch sizes, which directly translates to higher tokens-per-second throughput.

Why vLLM Matters

Before vLLM, serving LLMs was plagued by the “KV cache problem.” Traditional systems allocated contiguous memory for the key-value cache, leading to massive waste due to internal and external fragmentation. This meant that even if a GPU had free memory, the system could not fit more requests into a batch, limiting the overall efficiency of expensive hardware like NVIDIA A100s or H100s.

vLLM changes this by allowing the KV cache to be stored in non-contiguous physical memory blocks. This allows the engine to share memory between sequences with overlapping prefixes (such as system prompts) and pack requests more densely. For developers, this means lower cost-per-token, stable latency at scale, and the ability to serve more users simultaneously without adding more GPUs.

The project’s rapid adoption is evidenced by its massive community support from organizations like NVIDIA, Hugging Face, and IBM, and its seamless integration with the most popular open-source models on Hugging Face.

Key Features

• PagedAttention: A memory management system that treats the KV cache like paged virtual memory, reducing fragmentation to near zero and maximizing VRAM utilization.
• Continuous Batching: An advanced scheduling mechanism that merges new requests into existing GPU batches as soon as a token is generated, rather than waiting for a fixed batch window.
• OpenAI-Compatible API: A drop-in replacement for OpenAI’s API, allowing developers to switch from proprietary models to self-hosted open-source models without changing their application code.
• Multi-Hardware Support: Native support for NVIDIA GPUs, AMD GPUs (via ROCm), Intel GPUs/CPUs, and Google TPUs, ensuring portability across different cloud providers.
• Quantization Support: Integration with GPTQ, AWQ, FP8, and INT8/INT4, allowing models to run on smaller GPUs with minimal loss in precision.
• Speculative Decoding: A technique that uses a smaller “draft” model to predict multiple tokens, which are then verified by the larger model in a single pass, significantly reducing latency.
• Prefix Caching: Automatically caches repeated prompt prefixes (like system prompts or RAG context), delivering 10-100x lower time-to-first-token for repeated queries.
• Distributed Inference: Support for tensor parallelism and pipeline parallelism, enabling the serving of massive models (like Llama-3 70B or DeepSeek-V3) across multiple GPUs.

How vLLM Compares

When choosing an inference engine, the trade-off is usually between ease of setup and peak raw performance. vLLM is widely considered the best general-purpose baseline for production serving.

vLLM is the ideal choice for teams that need to deploy models quickly with high throughput and flexibility across different hardware. While TensorRT-LLM can achieve higher peak performance on specific NVIDIA cards, it requires a time-consuming model compilation step and is locked to NVIDIA hardware. TGI is a highly stable, ops-friendly choice, but vLLM’s PagedAttention often provides a better out-of-the-box experience for spiky, general-purpose chat workloads.

Getting Started: Installation

vLLM can be installed via pip or from source. It requires Python 3.10+ (3.12+ recommended) and a compatible GPU with CUDA 12.1+ installed.

Installation via pip

The fastest way to get started is using the uv package manager for faster installation:

uv pip install vllm

Or using standard pip:

pip install vllm

Installation via Docker

For production deployments, using the official Docker image is recommended to avoid dependency conflicts:

docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest

Building from Source

If you are developing for vLLM or need a specific hardware backend, you can build from source:

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e .

How to Use vLLM

vLLM provides two primary ways to use it: as a Python library for offline batched inference and as an OpenAI-compatible server for online serving.

Offline Inference

For processing a large dataset of prompts, use the LLM class. This is the simplest way to run a model locally without starting a server.

from vllm import LLM, SamplingParams

# Initialize the model
llm = LLM(model="facebook/opt-125m")

# Set sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Generate text
outputs = llm.generate(["The capital of France is"], sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"{prompt}: {generated_text}")

Online Serving

To launch an OpenAI-compatible API server, use the vllm serve command. This allows your application to connect to the model via HTTP requests.

vllm serve facebook/opt-125m

Once the server is running, you can send requests using the standard OpenAI Python client:

import openai

client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="token")

response = client.chat.completions.create(
    model="facebook/opt-125m",
    messages=[{"role": "user", "content": "Hello, vLLM!"}]
)
print(response.choices[0].message.content)

Code Examples

Below are examples of how to leverage vLLM’s advanced features for production-grade serving.

Example 1: Using Quantization to Save VRAM

You can run models in 4-bit or 8-bit precision to fit larger models on smaller GPUs. For example, using AWQ quantization:

# Launch server with AWQ quantization
vllm serve facebook/opt-125m --quantization awq

Example 2: Enabling Prefix Caching for RAG

If your application uses a long system prompt or a large context window for RAG, enable prefix caching to drastically reduce the time-to-first-token.

# Launch server with prefix caching enabled
vllm serve facebook/opt-125m --enable-prefix-caching

Example 3: Distributed Inference with Tensor Parallelism

To serve a model that is too large for a single GPU, split the model across multiple GPUs using the --tensor-parallel-size flag.

# Serve a 70B model across 4 GPUs
vllm serve meta-llama/Meta-Llama-3-70B --tensor-parallel-size 4

Advanced Configuration

vLLM offers extensive configuration options to tune performance based on your hardware and workload. The most critical parameters are those that control memory allocation and batching.

Memory Management Parameters

• --gpu-memory-utilization: Controls the percentage of GPU memory that vLLM will reserve for the KV cache. The default is 0.9 (90%). If you are running other processes on the GPU, lower this value to avoid Out-of-Memory (OOM) errors.
• --max-model-len: Sets the maximum sequence length the model can handle. Reducing this can save VRAM and allow for larger batch sizes.

Throughput Optimization

• --max-num-seqs: The maximum number of sequences that can be processed in a single iteration. Increasing this can improve throughput but may increase latency for individual requests.
• --enable-chunked-prefill: Splits long prompt prefill into chunks that interleave with decode steps from other requests, preventing long prompts from blocking the rest of the la-tency.

Real-World Use Cases

vLLM is designed for high-scale production environments where cost-efficiency and throughput are the primary goals.

• Enterprise RAG Pipelines: For companies building Retrieval-Augmented Generation systems, vLLM’s prefix caching is a game-changer. By caching the retrieved documents and system prompts, the system can serve thousands of users with significantly lower latency.
• High-Volume Chatbots: For startups building AI agents or customer service bots, vLLM’s continuous batching allows them to handle spiky traffic patterns without needing to over-provision GPU hardware.
• Large-Scale Model Evaluation: For researchers performing batch inference on millions of tokens, vLLM’s offline inference mode provides the fastest way to generate predictions without the overhead of an HTTP server.
• Multi-Modal Serving: With support for LLaVA and other multi-modal models, vLLM can be used to build vision-language applications that process images and text simultaneously at scale.

Contributing to vLLM

vLLM is a community-driven project that welcomes all kinds of contributions. Whether you are adding support for a new model, fixing a bug, or improving documentation, your help is needed.

The project follows the Google Python and C++ style guides. To contribute, you should first clone the repository, set up your Python virtual environment (using uv is recommended), and run the pre-commit hooks to ensure code quality.

To report a bug or request a feature, use GitHub Issues. For security vulnerabilities, please use the GitHub Security Advisories feature. For coordinating contributions and development, the community uses a dedicated Slack channel.

Community and Support

vLLM has one of the most active communities in the LLM ecosystem. You can find support and coordinate development through the following official channels:

• GitHub Discussions: The primary place for general questions and collaboration.
• Developer Slack: Used for coordinating contributions and discussing technical features.
• Official GitHub Repository: The source of truth for the codebase and issues.
• vLLM Forum: A dedicated space for discussing with fellow users.
• Twitter/X: For latest news and project updates.

Conclusion

vLLM is the essential tool for anyone moving from prototyping LLMs to production serving. By solving the memory fragmentation problem through PagedAttention, it transforms how we utilize GPU hardware, making high-throughput inference affordable and accessible.

If you need the absolute peak performance on a single NVIDIA card, TensorRT-LLM might be the a-lternative, but for 95% of use cases, vLLM’s flexibility, multi-hardware support, and ease of setup make it the la-sting choice. Star the repo, try the quickstart, and join the community to start serving your models at scale.

Resources

• vLLM GitHub Repository
• Official vLLM Documentation
• vLLM Official Website