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ML 컴파일을 지원하는 범용 LLM 배포 엔진

mlc-llm 최신버젼 다운로드

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# MLC LLM
[![Installation](https://img.shields.io/badge/docs-latest-green)](https://llm.mlc.ai/docs/)
[![License](https://img.shields.io/badge/license-apache\_2-blue)](https://github.com/mlc-ai/mlc-llm/blob/main/LICENSE)
[![Join Discoard](https://img.shields.io/badge/Join-Discord-7289DA?logo=discord&logoColor=white)](https://discord.gg/9Xpy2HGBuD)
[![Related Repository: WebLLM](https://img.shields.io/badge/Related\_Repo-WebLLM-fafbfc?logo=github)](https://github.com/mlc-ai/web-llm/)
\*\*ML 컴파일을 활용한 범용 LLM 배포 엔진\*\*
[시작하기](https://llm.mlc.ai/docs/get\_started/quick\_start) | [문서](https://llm.mlc.ai/docs) | [블로그](https://blog.mlc.ai/)

## 소개
MLC LLM은 대규모 언어 모델을 위한 머신러닝 컴파일러이자 고성능 배포 엔진입니다. 이 프로젝트의 목표는 모든 사람이 자신의 플랫폼에서 네이티브하게 AI 모델을 개발, 최적화 및 배포할 수 있도록 하는 것입니다.

|  | AMD GPU | NVIDIA GPU | Apple GPU | Intel GPU |
| --- | --- | --- | --- | --- |
| Linux / Win | ✅ Vulkan, ROCm | ✅ Vulkan, CUDA | 해당 없음 | ✅ Vulkan |
| macOS | ✅ Metal (dGPU) | 해당 없음 | ✅ Metal | ✅ Metal (iGPU) |
| Web Browser | ✅ WebGPU and WASM | | | |
| iOS / iPadOS | ✅ Metal on Apple A-series GPU | | | |
| Android | ✅ OpenCL on Adreno GPU | | ✅ OpenCL on Mali GPU | |

MLC LLM은 위 플랫폼들 전반에서 동작하는 통합 고성능 LLM 추론 엔진인 MLCEngine 위에서 코드를 컴파일하고 실행합니다. MLCEngine은 REST 서버, Python, JavaScript, iOS, Android를 통해 사용할 수 있는 OpenAI 호환 API를 제공하며, 이 모두 커뮤니티와 함께 지속적으로 개선해 나가는 동일한 엔진 및 컴파일러를 기반으로 합니다.
## 시작하기
MLC LLM을 시작하려면 [문서](https://llm.mlc.ai/docs/)를 방문해 주세요.
- [설치](https://llm.mlc.ai/docs/install/mlc\_llm)
- [빠른 시작](https://llm.mlc.ai/docs/get\_started/quick\_start)
- [소개](https://llm.mlc.ai/docs/get\_started/introduction)
## 인용
저희 프로젝트가 유용하다고 생각하신다면 인용을 고려해 주세요:
```bibtex
@software{mlc-llm,
author = {{MLC team}},
title = {{MLC-LLM}},
url = {https://github.com/mlc-ai/mlc-llm},
year = {2023-2025}
}
```
MLC LLM의 기반 기술은 다음과 같습니다:
참조 (펼치려면 클릭)
```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},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
pages = {35783--35796},
publisher = {Curran Associates, Inc.},
title = {Tensor Program Optimization with Probabilistic Programs},
url = {https://proceedings.neurips.cc/paper\_files/paper/2022/file/e894eafae43e68b4c8dfdacf742bcbf3-Paper-Conference.pdf},
volume = {35},
year = {2022}
}
@inproceedings{tvm,
author = {Tianqi Chen and Thierry Moreau and Ziheng Jiang and Lianmin Zheng and Eddie Yan and Haichen Shen and Meghan Cowan and Leyuan Wang and Yuwei Hu and Luis Ceze and Carlos Guestrin and Arvind Krishnamurthy},
title = {{TVM}: An Automated {End-to-End} Optimizing Compiler for Deep Learning},
booktitle = {13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)},
year = {2018},
isbn = {978-1-939133-08-3},
address = {Carlsbad, CA},
pages = {578--594},
url = {https://www.usenix.org/conference/osdi18/presentation/chen},
publisher = {USENIX Association},
month = oct,
}
```

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