NVIDIA/Model-Optimizer
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.
### Overview:
Model Optimizer is a unified library of state-of-the-art model optimization techniques, including quantization, pruning, Neural Architecture Search, distillation, speculative decoding, and sparsity. It is designed to accelerate deep learning models for deployment in frameworks like TensorRT-LLM, TensorRT, vLLM, and SGLang, and is integrated with Megatron-Bridge, Megatron-LM, and Hugging Face Accelerate for training and inference optimization.
### Key Features:
- **Quantization**: Compresses models for faster inference.
- **Pruning**: Reduces model size and complexity.
- **Neural Architecture Search (NAS)**: Automatically optimizes model architecture.
- **Distillation**: Retains model accuracy while reducing model size.
- **Speculative Decoding**: Enhances model performance during inference.
- **Sparsity**: Reduces model memory usage.
### Tech Stack:
Python
### Why It Matters:
Model Optimizer simplifies the optimization process for deep learning models, making it easier to deploy models with improved performance and reduced resource consumption. It is widely used in AI software ecosystems, enabling faster inference times and more efficient use of resources in applications like language models and inference frameworks.