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Universal LLM Deployment Engine with ML Compilation
| Date | Stars |
|---|---|
| 2026-07-31 | 23012 |
| 2026-08-01 | 23012 |
| 2026-08-03 | 23020 |
| 2026-08-06 | 23020 |
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<div align="center">
# MLC LLM
[](https://llm.mlc.ai/docs/)
[](https://github.com/mlc-ai/mlc-llm/blob/main/LICENSE)
[](https://discord.gg/9Xpy2HGBuD)
[](https://github.com/mlc-ai/web-llm/)
**Universal LLM Deployment Engine with ML Compilation**
[Get Started](https://llm.mlc.ai/docs/get_started/quick_start) | [Documentation](https://llm.mlc.ai/docs) | [Blog](https://blog.mlc.ai/)
</div>
## About
MLC LLM is a machine learning compiler and high-performance deployment engine for large language models. The mission of this project is to enable everyone to develop, optimize, and deploy AI models natively on everyone's platforms.
<div align="center">
<table style="width:100%">
<thead>
<tr>
<th style="width:15%"> </th>
<th style="width:20%">AMD GPU</th>
<th style="width:20%">NVIDIA GPU</th>
<th style="width:20%">Apple GPU</th>
<th style="width:24%">Intel GPU</th>
</tr>
</thead>
<tbody>
<tr>
<td>Linux / Win</td>
<td>✅ Vulkan, ROCm</td>
<td>✅ Vulkan, CUDA</td>
<td>N/A</td>
<td>✅ Vulkan</td>
</tr>
<tr>
<td>macOS</td>
<td>✅ Metal (dGPU)</td>
<td>N/A</td>
<td>✅ Metal</td>
<td>✅ Metal (iGPU)</td>
</tr>
<tr>
<td>Web Browser</td>
<td colspan=4>✅ WebGPU and WASM </td>
</tr>
<tr>
<td>iOS / iPadOS</td>
<td colspan=4>✅ Metal on Apple A-series GPU</td>
</tr>
<tr>
<td>Android</td>
<td colspan=2>✅ OpenCL on Adreno GPU</td>
<td colspan=2>✅ OpenCL on Mali GPU</td>
</tr>
</tbody>
</table>
</div>
MLC LLM compiles and runs code on MLCEngine -- a unified high-performance LLM inference engine across the above platforms. MLCEngine provides OpenAI-compatible API available through REST server, python, javascript, iOS, Android, all backed by the same engine and compiler that we keep improving with the community.
## Get Started
Please visit our [documentation](https://llm.mlc.ai/docs/) to get started with MLC LLM.
- [Installation](https://llm.mlc.ai/docs/install/mlc_llm)
- [Quick start](https://llm.mlc.ai/docs/get_started/quick_start)
- [Introduction](https://llm.mlc.ai/docs/get_started/introduction)
## Citation
Please consider citing our project if you find it useful:
```bibtex
@software{mlc-llm,
author = {{MLC team}},
title = {{MLC-LLM}},
url = {https://github.com/mlc-ai/mlc-llm},
year = {2023-2025}
}
```
The underlying techniques of MLC LLM include:
<details>
<summary>References (Click to expand)</summary>
```bibtex
@inproceedings{tensorir,
author = {Feng, Siyuan and Hou, Bohan and Jin, Hongyi and Lin, Wuwei and Shao, Junru and Lai, Ruihang and Ye, Zihao and Zheng, Lianmin and Yu, Cody Hao and Yu, Yong and Chen, Tianqi},
title = {TensorIR: An Abstraction for Automatic Tensorized Program Optimization},
year = {2023},
isbn = {9781450399166},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3575693.3576933},
doi = {10.1145/3575693.3576933},
booktitle = {Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},
pages = {804–817},
numpages = {14},
keywords = {Tensor Computation, Machine Learning Compiler, Deep Neural Network},
location = {Vancouver, BC, Canada},
series = {ASPLOS 2023}
}
@inproceedings{metaschedule,
author = {Shao, Junru and Zhou, Xiyou and Feng, Siyuan and Hou, Bohan and Lai, Ruihang and Jin, Hongyi and Lin, Wuwei and Masuda, Masahiro and Yu, Cody Hao and Chen, Tianqi},
Excerpt of 5,179 characters
Read on GitHub
Ruihang Lai · Carnegie Mellon University · United States
495
Junru Shao · United States
150
Tianqi Chen · CMU, NVIDIA
135
Charlie Ruan · UC Berkeley
87
Wuwei Lin · @openai · United States
71
Zihao Ye · United States
56
Yaxing Cai · NVIDIA · China
46
Rick Zhou · @linkedin
32
Eric Lunderberg · OctoML
32
Shushi Hong · Carnegie Mellon University · United States
30
David Pissarra · NYU · United States
30
Bohan Hou
28
Hongyi Jin · cmu · United States
27
26
25
Yixin Dong · Carnegie Mellon University · United States
24
24
Akaash Parthasarathy · Carnegie Mellon University · United States
18
Sudeep Agarwal · United States
15
Kathryn (Jinqi) Chen
14
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:905d4c36c1f01436, topic:llm, topic:language-model