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Model Optimizer is a unified library that provides state-of-the-art techniques for compressing and optimizing deep learning models to improve inference efficiency and deployment performance. It brings together multiple optimization strategies such as quantization, pruning, distillation, and speculative decoding into a single cohesive framework. The library is designed to reduce model size and computational requirements while maintaining accuracy, making it particularly valuable for deploying large models in production environments. It supports a wide range of model types, including large language models, diffusion models, and vision-language models, and integrates with deployment frameworks such as TensorRT and vLLM. By providing standardized workflows and APIs, it enables developers to experiment with different optimization strategies and select the best approach for their use case.
Features
- Unified library for quantization, pruning, and distillation
- Support for LLMs, diffusion models, and multimodal systems
- Integration with TensorRT, vLLM, and deployment frameworks
- Speculative decoding for faster inference
- Evaluation tools and support matrices for optimization methods
- Model compression for reduced memory and compute usage
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