Model-Optimizer

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Model-Optimizer is an Open Source & Models tool that optimizes deep learning models. It uses techniques like quantization, pruning, and distillation to accelerate inference speed for deployment.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Model-Optimizer?

NVIDIA Model Optimizer is an open-source library designed to accelerate deep learning models through various state-of-the-art optimization techniques. It supports quantization, pruning, distillation, speculative decoding, and sparsity to compress models and enhance inference speed. The tool accepts Hugging Face, PyTorch, or ONNX models as input and provides Python APIs for composing optimization techniques. Optimized checkpoints can be seamlessly exported for deployment in frameworks like SGLang, TensorRT-LLM, TensorRT, and vLLM, making it a crucial component within the NVIDIA AI software ecosystem for efficient model deployment.

Best used for

Ideal for developers who need to significantly reduce the size and improve the inference speed of their deep learning models, including LLMs and diffusers. Especially valuable for preparing models for efficient deployment on NVIDIA hardware and integrating with frameworks like TensorRT-LLM and vLLM.

Common actions

optimize deep learning models

accelerate AI inference

compress model size

deploy optimized models

quantize neural networks

open-sourceautomated workflowdeepfakecollaborationlow-code/no-codeworkflowsface swapping"AI Agents"github copilot

Capabilities

Key features

Post-training quantization
Quantization-aware training
Model pruning
Model distillation
Speculative decoding
Sparsity techniques
Hugging Face model export

Target Audience

developer

Integrations

hugging-facepytorchonnxnvidia-megatron-bridgenvidia-megatron-lmhugging-face-acceleratesglangtensorrt-llm+ 2 more

Pricing & Plans

Open Source

Free

FAQs

What types of models can be optimized using NVIDIA Model Optimizer?

NVIDIA Model Optimizer supports a variety of deep learning models, including those from Hugging Face, PyTorch, and ONNX. It is particularly effective for large language models (LLMs), diffusers, and vision-language models (VLMs), enabling significant performance improvements across different AI applications.

Which deployment frameworks are compatible with models optimized by NVIDIA Model Optimizer?

Optimized models generated by NVIDIA Model Optimizer are designed for seamless integration with several popular deployment frameworks. These include SGLang, TensorRT-LLM, TensorRT, and vLLM, ensuring efficient and accelerated inference in production environments.

What optimization techniques does Model Optimizer offer?

Model Optimizer provides a comprehensive suite of techniques to enhance model efficiency. These include post-training quantization, quantization-aware training, pruning, distillation, speculative decoding, and sparsity. Each technique aims to reduce model size and accelerate inference while maintaining or improving accuracy.

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This tool also appears in

AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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