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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
NVIDIA AITune is an inference toolkit designed for tuning and deploying Deep Learning models with a focus on NVIDIA GPUs.
| Date | Stars |
|---|---|
| 2026-07-31 | 280 |
| 2026-08-06 | 280 |
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15.0
growth rate 0.00%/day
<!-- # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 --> # NVIDIA AITune [](LICENSE) [](https://www.python.org/downloads/) [](https://pytorch.org/) **NVIDIA AITune** is an inference toolkit designed for tuning and deploying Deep Learning models with a focus on NVIDIA GPUs. It provides model tuning capabilities through compilation and conversion paths that can significantly improve inference speed and efficiency across various AI workloads including Computer Vision, Natural Language Processing, Speech Recognition, and Generative AI. The toolkit enables seamless tuning of PyTorch models and pipelines using various backends such as TensorRT, Torch-TensorRT, TorchAO, Torch Inductor, and ONNX Runtime through a single Python API. The resulting tuned models are ready for deployment in production environments. NVIDIA AITune works with your environment — relying first on your software versions — and selects the best-performing backend for your software and hardware setup, guiding you to supported technologies. **Note**: This is the first release. The API may change in future versions. **NOTICE AND DISCLAIMER: This software automatically retrieves, accesses or interacts with external materials. Those retrieved materials are not distributed with this software and are governed solely by separate terms, conditions and licenses. You are solely responsible for finding, reviewing and complying with all applicable terms, conditions, and licenses, and for verifying the security, integrity and suitability of any retrieved materials for your specific use case. This software is provided "AS IS", without warranty of any kind. The author makes no representations or warranties regarding any retrieved materials, and assumes no liability for any losses, damages, liabilities or legal consequences from your use or inability to use this software or any retrieved materials. Use this software and the retrieved materials at your own risk.** ## Features at Glance The distinct capabilities of NVIDIA AITune are summarized in the feature matrix: | Feature | Description | |-----------------------------|---------------------------------------------------------------------------------------------------------------------------| | Ease-of-use | Single line of code to run all possible tuning paths directly from your source code | | Wide Backend Support | Compatible with various tuning backends including TensorRT, Torch-TensorRT, TorchAO, Torch Inductor, and ONNX Runtime | | Model Tuning | Enhance the performance of models such as ResNET and BERT for efficient inference deployment | | Pipeline Tuning | Streamline Python code pipelines for models such as Stable Diffusion and Flux using seamless model wrapping and tuning | | Model Export and Conversion | Automate the process of exporting and converting models between various formats with focus on TensorRT, Torch-TensorRT, and ONNX Runtime | | Correctness Testing | Ensures tuned models produce correct outputs by validating on provided data samples | | Performance Profiling | Profiles models to select the optimal backend based on performance metrics such as latency and throughput | | Model Persistence | Save and load tuned models for production deployment with flexible storage options | | JIT tuning | Just-in-time tuning of a model or a pipeline witho
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b9cf839712d6e176, topic:inference, topic:tensorrt, topic:onnxruntime