MLC LLM

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Machine Learning Compilation for Large Language Models (MLC LLM) is a high-performance universal deployment solution that allows native deployment of any large language models with native APIs with compiler acceleration. The mission of this project is to enable everyone to develop, optimize and deploy AI models natively on everyone's devices with ML compilation techniques.

Universal deployment. MLC LLM supports the following platforms and hardware:

	AMD GPU	NVIDIA GPU	Apple M1/M2 GPU	Intel GPU
Linux / Win	✅ Vulkan, ROCm	✅ Vulkan, CUDA	N/A	✅ Vulkan
macOS	✅ Metal	N/A	✅ Metal	✅ Metal
Web Browser	✅ WebGPU	✅ WebGPU	✅ WebGPU	✅ WebGPU
iOS / iPadOS	✅ Metal on Apple M1/M2 GPU
Android	✅ OpenCL on Adreno GPU		✅ OpenCL on Mali GPU

News

• [08/25/2023] CodeLlama support is up.
• [08/14/2023] [Post] Mali GPU support is up on Orange Pi.
• [08/09/2023] [Post] ROCm backend is mature to use.
• [08/02/2023] Dockerfile is released for CUDA performance benchmarking.
• [07/19/2023] Support for Llama2-7B/13B/70B is up.
• [05/22/2023] [Post] RedPajama support is up.
• [05/08/2023] [Post] MLC LLM is now available on Android.
• [05/01/2023] [Post] MLC LLM is released with Metal, Vulkan and CUDA backends.
• [04/14/2023] WebLLM is released prior to MLC LLM with WebGPU and WebAssembly backend.

Getting Started

Please visit our this page for detailed instructions.

Universal Deployment APIs

MLC LLM provides multiple sets of APIs across platforms and environments. These include

• Python API
• OpenAI-compatible Rest-API
• C++ API
• JavaScript API and Web LLM
• Swift API for iOS App
• Java API and Android App

Citation

Please consider citing our project if you find it useful:

@software{mlc-llm,
    author = {MLC team},
    title = {{MLC-LLM}},
    url = {https://github.com/mlc-ai/mlc-llm},
    year = {2023}
}

The underlying techniques of MLC LLM include:

References (Click to expand)

@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,
}

Links

• You might want to check out our online public Machine Learning Compilation course for a systematic walkthrough of our approaches.
• WebLLM is a companion project using MLC LLM's WebGPU and WebAssembly backend.
• WebStableDiffusion is a companion project for diffusion models with the WebGPU backend.