https://github.com/chrisdonahue/mlc-llm

Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.

https://github.com/chrisdonahue/mlc-llm

Repository

Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.

https://github.com/chrisdonahue/mlc-llm/blob/main/

[discord-url]: https://discord.gg/9Xpy2HGBuD

# MLC LLM

[Documentation](https://llm.mlc.ai/docs) | [Blog](https://blog.mlc.ai/) | [Discord][discord-url]

**M**achine **L**earning **C**ompilation for **L**arge **L**anguage **M**odels (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 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
    
  



**Scalable.** MLC LLM scales universally on NVIDIA and AMD GPUs, cloud and gaming GPUs. Below
showcases our single batch decoding performance with prefilling = 1 and decoding = 256.

Performance of 4-bit CodeLlama-34B and Llama2-70B on two NVIDIA RTX 4090 and two AMD Radeon 7900 XTX:

  
  


Scaling of fp16 and 4-bit CodeLlama-34 and Llama2-70B on A100-80G-PCIe and A10G-24G-PCIe, up to 8 GPUs:

  


## News

* [10/18/2023] [[Post]](https://blog.mlc.ai/2023/10/19/Scalable-Language-Model-Inference-on-Multiple-NVDIA-AMD-GPUs) Scalable multi-GPU support for CUDA and ROCm are official.
* [09/02/2023] Prebuilt ROCm 5.7 and CUDA 12.2 package is [available](https://llm.mlc.ai/docs/install/tvm.html#option-1-prebuilt-package).
* [08/25/2023] CodeLlama support is up.
* [08/14/2023] [[Post]](https://blog.mlc.ai/2023/08/09/GPU-Accelerated-LLM-on-Orange-Pi) Mali GPU support is up on Orange Pi.
* [08/09/2023] [[Post]](https://blog.mlc.ai/2023/08/09/Making-AMD-GPUs-competitive-for-LLM-inference) ROCm backend is mature to use.
* [08/02/2023] [Dockerfile](https://github.com/mlc-ai/llm-perf-bench/) is released for CUDA performance benchmarking.
* [07/19/2023] Support for Llama2-7B/13B/70B is up.
* [05/22/2023] [[Post]](https://blog.mlc.ai/2023/05/22/bringing-open-large-language-models-to-consumer-devices) RedPajama support is up.
* [05/08/2023] [[Post]](https://blog.mlc.ai/2023/05/08/bringing-hardware-accelerated-language-models-to-android-devices) MLC LLM is now available on Android.
* [05/01/2023] [[Post]](https://blog.mlc.ai/2023/05/01/bringing-accelerated-llm-to-consumer-hardware) MLC LLM is released with Metal, Vulkan and CUDA backends.
* [04/14/2023] [WebLLM](https://github.com/mlc-ai/web-llm) is released prior to MLC LLM with WebGPU and WebAssembly backend.

## Getting Started

Please visit our [documentation](https://llm.mlc.ai/docs/index.html#getting-started) for detailed instructions.

## Model Support

MLC LLM supports a wide range of model architectures and variants. We have the following prebuilts which you can
use off-the-shelf. Visit [Prebuilt Models](https://llm.mlc.ai/docs/prebuilt_models.html) to see the full list, and [Compile Models via MLC](https://llm.mlc.ai/docs/compilation/compile_models.html) to see how to use models not on this list.


  

    

      
Architecture
      
Prebuilt Model Variants
    
  
  

    

      
Llama
      
Llama-2, Code Llama, Vicuna, WizardLM, WizardMath, OpenOrca Platypus2, FlagAlpha Llama-2 Chinese, georgesung Llama-2 Uncensored
    
    

      
GPT-NeoX
      
RedPajama
    
    

      
GPT-J
      

    
    

      
RWKV
      
RWKV-raven
    
    

      
MiniGPT
      

    
    

      
GPTBigCode
      
WizardCoder
    
    

      
ChatGLM
      

    
    

      
StableLM
      

    
  


## Universal Deployment APIs

MLC LLM provides multiple sets of APIs across platforms and environments. These include
* [Python API](https://llm.mlc.ai/docs/deploy/python.html)
* [OpenAI-compatible Rest-API](https://llm.mlc.ai/docs/deploy/rest.html)
* [C++ API](https://llm.mlc.ai/docs/deploy/cli.html)
* [JavaScript API](https://llm.mlc.ai/docs/deploy/javascript.html) and [Web LLM](https://github.com/mlc-ai/web-llm)
* [Swift API for iOS App](https://llm.mlc.ai/docs/deploy/ios.html)
* [Java API and Android App](https://llm.mlc.ai/docs/deploy/android.html)

## 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}
}
```

The underlying techniques of MLC LLM include:


  
References (Click to expand)
  
  ```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 = {804817},
      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](https://mlc.ai) for a systematic
walkthrough of our approaches.
- [WebLLM](https://webllm.mlc.ai/) is a companion project using MLC LLM's WebGPU and WebAssembly backend.
- [WebStableDiffusion](https://websd.mlc.ai/) is a companion project for diffusion models with the WebGPU backend.

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