NVIDIA/TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
4 releases in the last 90 days, the latest 27 days ago, with 227 contributors on GitHub.
Gained 13% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Accelerates Transformer model training and inference on NVIDIA GPUs using 8-bit and 4-bit floating point precision for improved performance and reduced memory usage.
- Enables FP8 precision support on Hopper, Ada, and Blackwell GPUs with optimized kernels and seamless integration into popular deep learning frameworks.
- Use for building and training large language models with mixed precision to achieve faster throughput and lower memory consumption on NVIDIA hardware.
- Use for integrating FP8 support into custom deep learning libraries or frameworks via its framework-agnostic C++ API for advanced Transformer optimizations.
- Use for experimenting with state-of-the-art Transformer architectures in PyTorch or JAX with easy-to-use modules and automatic mixed precision APIs.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
.. Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Quickstart <#examples>_ |Installation <#installation>_ |User Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html>_ |Examples <https://github.com/NVIDIA/TransformerEngine/tree/main/examples>_ |FP8 Convergence <#fp8-convergence>_ |Integrations <#integrations>_ |Release notes <https://docs.nvidia.com/deeplearning/transformer-engine/documentation-archive.html>_.. image:: docs/examples/comparison-fp8-bf16-training-nvidia-dgx-cloud-benchmarking-performance-explorer.jpg :width: 600 :alt: Comparison of FP8 versus BF16 training, as seen in NVIDIA DGX Cloud Benchmarking Performance Explorer
Previous News <#previous-news>_
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
885+91
Open issues
376+54
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| onnx/onnx | 21.6K | — | 63 | 31 days ago |
| mlc-ai/web-llm | 19.3K | — | 59 | 16 days ago |
| lukas-blecher/LaTeX-OCR | 16.6K | — | 18 | — |
| milesial/Pytorch-UNet | 11.7K | — | 18 | — |
| NVIDIA/TransformerEngine | 3.6K | — | 59 | 27 days ago |
| SharpAI/DeepCamera | 3.1K | — | 31 | 227 days ago |
| Category median | 16.6K | — | 31 | — |
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