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.
| AMD GPU | NVIDIA GPU | Apple GPU | Intel GPU | |
|---|---|---|---|---|
| Linux / Win | ✅ Vulkan, ROCm | ✅ Vulkan, CUDA | N/A | ✅ Vulkan |
| macOS | ✅ Metal (dGPU) | N/A | ✅ 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 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.
Please visit our documentation to get started with MLC LLM.
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)
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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},
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pages = {804–817},
numpages = {14},
keywords = {Tensor Computation, Machine Learning Compiler, Deep Neural Network},
location = {Vancouver, BC, Canada},
series = {ASPLOS 2023}
}
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pages = {35783--35796},
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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,
}