MLC LLM

What is the project about?

MLC LLM is a universal deployment engine for large language models (LLMs) that uses machine learning compilation (MLC) techniques. It allows for native deployment of AI models across a wide variety of platforms.

What problem does it solve?

It addresses the challenge of deploying LLMs efficiently and universally. Typically, deploying LLMs is platform-specific and requires significant optimization effort for each target. MLC LLM aims to simplify this by providing a unified engine and compilation process.

What are the features of the project?

• Universal Deployment: Supports a wide range of hardware, including AMD, NVIDIA, Apple, and Intel GPUs, as well as web browsers (via WebGPU and WASM), iOS/iPadOS, and Android devices.
• ML Compilation: Uses machine learning compilation to optimize LLM performance for each target platform.
• Unified High-Performance Engine (MLCEngine): Provides a consistent inference engine across all supported platforms.
• OpenAI-Compatible API: Offers an API compatible with OpenAI, making it easy to integrate with existing tools and workflows. This API is accessible via REST, Python, JavaScript, iOS, and Android.

What are the technologies used in the project?

• Vulkan: A cross-platform graphics and compute API.
• ROCm: AMD's open-source platform for GPU-accelerated computing.
• CUDA: NVIDIA's parallel computing platform and API.
• Metal: Apple's graphics and compute framework.
• WebGPU: A web standard for GPU access in browsers.
• WASM (WebAssembly): A binary instruction format for web browsers.
• OpenCL: A framework for writing programs that execute across heterogeneous platforms.
• TVM: Deep Learning Compiler.
• TensorIR: An abstraction for automatic tensorized program optimization.
• MetaSchedule: Tensor Program Optimization with Probabilistic Programs.

What are the benefits of the project?

• Broad Hardware Support: Runs LLMs on a wide variety of devices, maximizing accessibility.
• Performance Optimization: ML compilation techniques improve inference speed and efficiency.
• Simplified Deployment: The unified engine and API reduce the complexity of deploying LLMs.
• Developer Friendly: The OpenAI-compatible API makes integration easier.
• Community Driven: Open Source project.

What are the use cases of the project?

• Running LLMs on edge devices: Deploying LLMs on mobile phones, tablets, and web browsers.
• Accelerating LLM inference: Improving the performance of LLMs on various GPUs.
• Developing LLM-powered applications: Providing a foundation for building applications that utilize LLMs across different platforms.
• Researching LLM optimization: Serving as a platform for exploring and developing new techniques for LLM compilation and deployment.