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Transformer Engine

Free

A library for accelerating Transformer model training on NVIDIA GPUs.

FreeFree tier
Type
Open Source
Company
NVIDIA
Visit Website GitHub (38 stars)

About Transformer Engine

Transformer Engine (TE) is an open-source library developed by NVIDIA for accelerating Transformer models on NVIDIA GPUs, including support for 8-bit floating point (FP8) precision on Hopper, Ada, and Blackwell architectures. It also introduces NVFP4 and MXFP8 formats on Blackwell GPUs to further improve efficiency and memory utilization. TE provides highly optimized building blocks for popular Transformer architectures, an automatic mixed precision-like API for seamless integration with framework-specific code, and a framework-agnostic C++ API for adding FP8 support to other deep learning libraries. It is designed to reduce memory and compute demands for large-scale models such as large language models, mixture-of-experts (MoE) architectures, and multimodal models during both training and inference.

Key Features

Supports FP8 precision on Hopper, Ada, and Blackwell GPUs for improved performance and lower memory usage
Supports NVFP4 (4-bit floating point) and MXFP8 (Microscaling FP8) on Blackwell GPUs for even greater efficiency
Provides highly optimized building blocks for popular Transformer architectures
Automatic mixed precision-like API for seamless integration with existing framework code
Framework-agnostic C++ API for enabling FP8 support in other deep learning libraries
Optimized for both training and inference of large-scale models (LLMs, MoE, multimodal)

Pros & Cons

Pros
  • Delivers significant speedups and lower memory utilization compared to FP32/FP16 training
  • Leverages latest NVIDIA GPU architectures (Hopper, Ada, Blackwell) for optimal performance
  • Open-source and freely available on GitHub
  • Easy integration with popular frameworks like PyTorch and JAX via automatic mixed precision-like API
  • Actively maintained by NVIDIA with regular updates and community support
  • Supports both training and inference with the same optimized kernels
Cons
  • Requires compatible NVIDIA GPUs (Hopper, Ada, or Blackwell) to utilize FP8/NVFP4 features
  • Primarily focused on Transformer architectures; limited support for other model types
  • May require additional tuning to achieve optimal accuracy when using very low precision (e.g., NVFP4)

Best For

Training and inference of large language models (LLMs) with reduced memory and compute requirementsScaling mixture-of-experts (MoE) architectures to hundreds of billions of parametersEfficient training of multimodal Transformer modelsReinforcement learning (RL) training with FP8 precisionHigh-throughput inference in production environments using lower precision formats

FAQ

What GPU architectures does Transformer Engine support?
Transformer Engine supports NVIDIA Hopper, Ada, and Blackwell GPU architectures for FP8 precision, with additional NVFP4 and MXFP8 support on Blackwell GPUs.
Is Transformer Engine free to use?
Yes, Transformer Engine is open-source and available for free on GitHub under an NVIDIA license.