MLC LLM: Universal Deployment Engine for LLMs on Any Platform

Large language models (LLMs) are now powering everything from chatbots to code assistants. Yet, running them locally—across PCs, mobile devices, or even browsers—remains a painful pain point. MLC LLM solves that problem by acting as a machine‑learning compiler that turns any LLM into a high‑performance, cross‑platform inference engine.

Why MLC LLM Matters

• Zero‑Cost Cloud‑Free Inference – No GPU‑as‑a‑Service subscription required.
• Unified Code Base – Write once, run anywhere: Windows, Linux, macOS, iOS, Android, WebGPU.
• Native Performance – Harness Vulkan on desktops, Metal on Apple silicon, CUDA/ROCm on NVIDIA/AMD, and WebGPU on browsers.
• Open‑Source Community – 20K+ stars on GitHub, >150 contributors, and an active issue tracker.

Core Architecture

Input Model (ONNX / PyTorch / TensorFlow) → 
  TensorIR ↔ MLC Compiler ↔ MLCEngine kernels → 
  Runtime (REST/API/JS/Swift/Kotlin) 

1. TensorIR – A lower‑level IR that captures tensor operations and their locality.
2. MLC Compiler – Applies TensorIR optimizations, schedule transformations, and platform‑specific code generation.
3. MLCEngine – A lightweight, thread‑safe inference engine that exposes an OpenAI‑compatible REST API, a Python module, and native bindings for iOS/Android.

The compiler leverages proven research such as TensorIR, MetaSchedule, and TVM to generate efficient kernels. It also features probabilistic program optimization to automatically discover the best schedule for a given GPU.

Supported Platforms & GPUs

Tip: Even on laptops without dedicated GPUs, MLC LLM can run in CPU mode with a performance penalty, making it useful for quick prototyping.

Quick Start – From Repository to REST API

```bash

1. Clone the repo

git clone https://github.com/mlc-ai/mlc-llm.git cd mlc-llm

2. Build the engine (requires CMake, Clang, and SDKs for your target platform)

For example, on Linux with CUDA:

./scripts/build_python.sh --cuda

3. Install the Python package

pip install .

4. Launch the REST server

mlc_llm serve --model meta-llama/Llama-2-7b-chat-hf

5. Query the model

curl -X POST http://127.0.0.1:8000/v1/chat/completions \ -H 'Content-Type: application/json' \ -d '{