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NVIDIA MODEL OPTIMIZER
Optimize and compress AI models for faster inference
Apache-2.0
ABOUT
Deploying large AI models is expensive and slow without optimization. Standard models require significant GPU memory and compute, making inference costly. NVIDIA Model Optimizer provides a comprehensive suite of compression techniques — quantization, pruning, distillation, NAS, and speculative decoding — that reduce model size and accelerate inference while preserving accuracy. The optimized models deploy seamlessly into production frameworks like TensorRT-LLM, vLLM, and SGLang.
INSTALL
pip install nvidia-modeloptINTEGRATION GUIDE
1. Quantize LLMs from FP16 to FP8 or INT4 for 2-4x faster inference with minimal accuracy loss using post-training quantization
2. Prune and distill large models into smaller ones — e.g., compressing a 355B model to 260B while retaining 90%+ quality
3. Apply neural architecture search to find optimal model configurations for target latency and memory budgets
4. Generate quantized checkpoints that are directly deployable to vLLM, TensorRT-LLM, or SGLang without manual conversion
5. Optimize vision models and diffusion models for production deployment on NVIDIA GPUs with TensorRT integration
TAGS
quantizationpruningdistillationmodel-optimizationinferencetensorrtnvidiapython