qualcomm/aimet

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.

PyTorch native quantization for training and inference

Brevitas: neural network quantization in PyTorch

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Model compression toolkit engineered for enhanced usability, comprehensiveness, and efficiency.

Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.

State-of-the-art LLM compression, built for production inference with vLLM

PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT