apple/coreai-optimization

FasterAI: Prune and Distill your models with FastAI and PyTorch

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

Bridges PyTorch and Core AI. Convert existing models to Core AI IR, or author new ones from PyTorch via composite ops, custom op lowerings, and inline Metal GPU kernels.

PyTorch native quantization for training and inference

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

Neural network quantization for research and prototyping

PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT

JANG — GGUF for MLX. YOU MUST USE JANG_Q RUNTIME. Adaptive Mixed-Precision Quantization + Runtime for Apple Silicon