# vLLM review

> vLLM is an open-source Python library and engine for high-throughput, memory-efficient inference and serving of large language models.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/vllm/

## TL;DR

vLLM is a library for LLM inference and serving that began at UC Berkeley's Sky Computing Lab. vLLM is for teams serving Hugging Face models on GPUs, CPUs, or other accelerators. What sets vLLM apart is PagedAttention memory management, continuous batching, and an OpenAI-compatible API server.

## vLLM at a glance

- **Category:** [Inference and training](https://index.devtoolwatch.com/categories/inference-and-training/)
- **Maintained by:** vLLM (organization, [@vllm-project](https://github.com/vllm-project) on GitHub)
- **License:** Apache 2.0
- **Language:** Python, Rust, Cuda
- **Repository:** [github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)
- **Website:** [vllm.ai](https://vllm.ai)
- **PyPI package:** [vllm](https://pypi.org/project/vllm/) 0.32.0
- **Latest release:** [v0.32.0](https://github.com/vllm-project/vllm/releases/tag/v0.32.0) on GitHub, Oct 18, 2026
- **First published:** Feb 9, 2023

## What vLLM does

- vLLM manages attention key and value memory with PagedAttention and uses continuous batching, chunked prefill, and prefix caching for incoming requests.
- vLLM runs an OpenAI-compatible API server and also supports the Anthropic Messages API and gRPC.
- vLLM supports quantization formats including FP8, INT8, INT4, GPTQ/AWQ, GGUF, and compressed-tensors.
- vLLM offers tensor, pipeline, data, expert, and context parallelism for distributed inference.
- vLLM provides speculative decoding methods, including n-gram, suffix, and EAGLE.
- vLLM generates structured outputs using xgrammar or guidance and includes tool calling and reasoning parsers.
- vLLM supports efficient multi-LoRA serving for dense and MoE layers, plus streaming outputs.

### How it works

vLLM installs as a Python package and loads models from Hugging Face. These include decoder-only, mixture-of-experts, multimodal, embedding, and reward models. vLLM serves them through an OpenAI-compatible API server. vLLM runs on NVIDIA, AMD, and Intel GPUs and on x86, ARM, and PowerPC CPUs. Hardware plugins add Google TPUs, Intel Gaudi, Huawei Ascend, Apple Silicon, and others.

## How to install vLLM

**With uv**

```
uv pip install vllm
```

**With uv, automatic torch backend**

```
uv pip install vllm --torch-backend auto
```

**Download precompiled kernels**

```
vllm download-kernels
```

### Requirements

- vLLM 0.32.0 requires Python 3.11 or newer and below 3.15.
- vLLM needs a supported accelerator or CPU, such as an NVIDIA, AMD, or Intel GPU or an x86/ARM/PowerPC CPU.

### First steps

1. Install vLLM with uv, which the project recommends over pip.
2. Run vllm download-kernels to fetch precompiled kernels for faster startup.
3. Follow the Quickstart guide at docs.vllm.ai and check the supported models list.

## Who vLLM is for

- Teams serving open-source LLMs behind an OpenAI-compatible API.
- Developers running large mixture-of-experts models across multiple GPUs with distributed parallelism.
- Researchers studying LLM serving memory management and scheduling.
- Operators deploying on mixed hardware, including AMD ROCm, TPUs, or Intel Gaudi.

### Limitations

- vLLM supports some hardware, such as TPUs, Gaudi, and Apple Silicon, through separate plugins rather than the core package.
- vLLM's Python requirements differ between sources: PyPI states 3.11 to below 3.15, while the homepage says 3.10+.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #56 overall, #9 in Inference and training, momentum score 74
- Downloads that week: 677.2k (+43% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 627; Aug 16: 639; Aug 23: 738; Aug 30: 627; Sep 6: 589; Sep 13: 628; Sep 20: 558; Sep 27: 425; Oct 4: 449; Oct 11: 525; Oct 18: 1.4k; Oct 25: 668

## vLLM alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [dsh-our-free-model](https://index.devtoolwatch.com/tools/ebony-vinyl-dsh-our-free-model/) | 54.3k | – |
| #2 | [magpie](https://index.devtoolwatch.com/tools/yetone-magpie/) | 26.9k | – |
| #3 | [Strata](https://index.devtoolwatch.com/tools/niko1221-strata/) | 37.1k | – |
| #4 | [edge0](https://index.devtoolwatch.com/tools/edge0-ai-edge0/) | 8.9k | – |
| #5 | [OmniRoute](https://index.devtoolwatch.com/tools/diegosouzapw-omniroute/) | 83.2k | 106k |
| #6 | [CLIProxyAPI](https://index.devtoolwatch.com/tools/router-for-me-cliproxyapi/) | 57.1k | – |

## vLLM FAQ

### Is vLLM free and open source?

Yes. vLLM's source code is on GitHub (vllm-project/vllm) under the Apache 2.0 license, so you can use it for free.

### How do I install vLLM?

Run `uv pip install vllm`. The vllm package needs Python 3.11 or newer, below 3.15. The install section above has the other options from the project's README.

### Is vLLM still maintained?

Yes. The vllm-project/vllm repository was last updated on Oct 25, 2026, and the latest release, v0.32.0 on GitHub, came out on Oct 18, 2026.

### How popular is vLLM?

vLLM has 96k GitHub stars as of Nov 1, 2026. It gained 668 new stars in the week of Oct 25–31, 2026, against a 12-week average of 658 a week. Its main package had 677.2k downloads that week, +43% against its own 4-week average. That made it #9 among LLM inference and training tools on the Devtoolwatch Board.

### vLLM vs llama.cpp: how do they compare?

vLLM and llama.cpp are both tracked by Devtoolwatch as LLM inference and training tools. vLLM has 96k GitHub stars and llama.cpp has 133k. llama.cpp is a plain C/C++ library and set of tools for running LLM and VLM inference locally or in the cloud on many kinds of hardware. [llama.cpp review](https://index.devtoolwatch.com/tools/llama-cpp/)

### vLLM vs Ollama: how do they compare?

vLLM and Ollama are both tracked by Devtoolwatch as LLM inference and training tools. vLLM has 96k GitHub stars and Ollama has 185.5k. In the week of Oct 25–31, 2026, vLLM's main package had 677.2k downloads and Ollama's had 4M. Ollama is a tool for running and building with open AI models locally or in the cloud on macOS, Windows, and Linux. [Ollama review](https://index.devtoolwatch.com/tools/ollama/)

### What are the best alternatives to vLLM?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

### Is vLLM an inference engine?

Yes. vLLM describes itself as a high-throughput and memory-efficient inference and serving engine for LLMs.

### Is vLLM written in Python?

Yes. vLLM is mainly Python. The repository also contains Rust, CUDA, C++, and Shell code.

### How do I use vLLM with Hugging Face models?

vLLM integrates with Hugging Face models. It supports decoder-only, mixture-of-experts, multimodal, embedding, and reward models. The full list is in the documentation.

### Can vLLM run on a Mac?

vLLM lists Apple Silicon among its hardware plugins. Apple Silicon support comes from a plugin rather than core GPU support.

### Does vLLM provide an OpenAI-compatible API?

Yes. vLLM includes an OpenAI-compatible API server. It also supports the Anthropic Messages API and gRPC.

## Sources

1. [vllm-project/vllm on GitHub](https://github.com/vllm-project/vllm)
2. [vLLM README](https://github.com/vllm-project/vllm/blob/main/README.md)
3. [vllm on PyPI](https://pypi.org/project/vllm/)
4. [vLLM website](https://vllm.ai)
5. [Release v0.32.0 (Oct 18, 2026)](https://github.com/vllm-project/vllm/releases/tag/v0.32.0)
6. [Release v0.31.0 (Oct 5, 2026)](https://github.com/vllm-project/vllm/releases/tag/v0.31.0)
7. [Release v0.30.0 (Sep 22, 2026)](https://github.com/vllm-project/vllm/releases/tag/v0.30.0)
8. [Hacker News: vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention](https://news.ycombinator.com/item?id=36409082)
9. [Hacker News: vLLM large scale serving: DeepSeek 2.2k tok/s/h200 with wide-ep](https://news.ycombinator.com/item?id=46602737)
10. [Hacker News: Speculative Decoding in vLLM on AMD GPUs](https://news.ycombinator.com/item?id=49596054)
11. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)