Skip to content

docs(readme): update convergence table, latest news, and outdated links #2638

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
sbhavani wants to merge 3 commits into
base: main
Choose a base branch
from
+10 −18
Open

docs(readme): update convergence table, latest news, and outdated links #2638

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
eb6bb88
docs(readme): update FP8 convergence table and add MXFP8/NVFP4 suppor…
sbhavani Feb 1, 2026
d5922a3
fix(readme): address review feedback
sbhavani Feb 2, 2026
52deeda
Revert FP8 Primer link changes, defer to PR #2641
sbhavani Feb 10, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 10 additions & 18 deletions README.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@ Transformer Engine
Latest News
===========

* [12/2025] `NVIDIA Nemotron 3: Efficient and Open Intelligence <https://arxiv.org/abs/2512.20856>`_ - trained with NVFP4 on Transformer Engine
* [11/2025] `NVIDIA Blackwell Architecture Sweeps MLPerf Training v5.1 Benchmarks <https://developer.nvidia.com/blog/nvidia-blackwell-architecture-sweeps-mlperf-training-v5-1-benchmarks/>`_
* [11/2025] `Scale Biology Transformer Models with PyTorch and NVIDIA BioNeMo Recipes <https://developer.nvidia.com/blog/scale-biology-transformer-models-with-pytorch-and-nvidia-bionemo-recipes/>`_
* [11/2025] `FP8 Training of Large-Scale RL Models <https://lmsys.org/blog/2025-11-25-fp8-rl/>`_
Expand All @@ -30,7 +31,8 @@ What is Transformer Engine?

Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including
using 8-bit floating point (FP8) precision on Hopper, Ada, and Blackwell GPUs, to provide better
performance with lower memory utilization in both training and inference. TE provides a collection
performance with lower memory utilization in both training and inference. On Blackwell GPUs, TE also
supports MXFP8 (Microscaling FP8) and NVFP4 formats for even greater efficiency. TE provides a collection
of highly optimized building blocks for popular Transformer architectures and an automatic mixed
precision-like API that can be used seamlessly with your framework-specific code. TE also includes a
framework agnostic C++ API that can be integrated with other deep learning libraries to enable FP8
Expand Down Expand Up @@ -58,6 +60,7 @@ Highlights
* Easy-to-use modules for building Transformer layers with FP8 support
* Optimizations (e.g. fused kernels) for Transformer models
* Support for FP8 on NVIDIA Hopper, Ada, and Blackwell GPUs
* Support for MXFP8 and NVFP4 on NVIDIA Blackwell GPUs
* Support for optimizations across all precisions (FP16, BF16) on NVIDIA Ampere GPU architecture generations and later

Examples
Expand Down Expand Up @@ -190,7 +193,7 @@ We recommend updating to the latest NGC container available here:
* https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch
* https://catalog.ngc.nvidia.com/orgs/nvidia/containers/jax

If you run any examples, please ensure you are using a matching version of TransformerEngine. TransformerEngine is pre-built and packaged inside the containers with examples available at ``/opt/transformerengine`` or ``/opt/transformer-engine``. If you would like to use examples from TE main branch and are running into import errors, please try the latest pip package or building from source, although NGC containers are recommended for ease-of-use for most users.
If you run any examples, please ensure you are using a matching version of TransformerEngine. TransformerEngine is pre-built and packaged inside the containers with examples available at ``/opt/transformerengine`` or ``/opt/transformer-engine``.

**Benefits of using NGC containers:**

Expand Down Expand Up @@ -381,46 +384,35 @@ FP8 has been tested extensively across different model architectures and configu
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| Model | Framework | Source |
+============+==================+=========================================================================================================+
| T5-770M | JAX/T5x | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/t5x#convergence-and-performance|
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| MPT-1.3B | Mosaic Composer | https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1 |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-5B | JAX/Paxml | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/pax#h100-results |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-5B | NeMo Framework | Available on request |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| LLama2-7B | Alibaba Pai | https://mp.weixin.qq.com/s/NQT0uKXLbXyh5031zBdeBQ |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| T5-11B | JAX/T5x | Available on request |
| LLM-8B | Megatron Core | https://arxiv.org/abs/2506.08027 |
Copy link
Contributor

@greptile-apps greptile-apps bot Feb 2, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

extra space before "Megatron Core" - should have 2 spaces like other rows, not 3

Suggested change
| LLM-8B | Megatron Core | https://arxiv.org/abs/2506.08027 |
| LLM-8B | Megatron Core | https://arxiv.org/abs/2506.08027 |

+------------+------------------+---------------------------------------------------------------------------------------------------------+
| MPT-13B | Mosaic Composer | https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8 |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-22B | NeMo Framework | Available on request |
| MoE-16B | Megatron Core | https://arxiv.org/abs/2506.08027 |
Copy link
Contributor

@greptile-apps greptile-apps bot Feb 2, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

extra space before "Megatron Core" - should have 2 spaces like other rows, not 3

Suggested change
| MoE-16B | Megatron Core | https://arxiv.org/abs/2506.08027 |
| MoE-16B | Megatron Core | https://arxiv.org/abs/2506.08027 |

+------------+------------------+---------------------------------------------------------------------------------------------------------+
| LLama2-70B | Alibaba Pai | https://mp.weixin.qq.com/s/NQT0uKXLbXyh5031zBdeBQ |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| GPT-175B | JAX/Paxml | https://github.com/NVIDIA/JAX-Toolbox/tree/main/rosetta/rosetta/projects/pax#h100-results |
+------------+------------------+---------------------------------------------------------------------------------------------------------+

Integrations
============

Transformer Engine has been integrated with popular LLM frameworks such as:

* `DeepSpeed <https://github.com/deepspeedai/DeepSpeed/blob/master/tests/unit/runtime/half_precision/test_fp8.py>`_
* `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_
* `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/main/en/usage_guides/low_precision_training#configuring-transformersengine>`_
* `Lightning <https://github.com/Lightning-AI/lightning/issues/17172>`_
* `Lightning <https://lightning.ai/docs/pytorch/stable/common/precision.html>`_
* `MosaicML Composer <https://github.com/mosaicml/composer/releases/tag/v0.13.1>`_
* `NVIDIA JAX Toolbox <https://github.com/NVIDIA/JAX-Toolbox>`_
* `NVIDIA Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_
* `NVIDIA NeMo Framework <https://github.com/NVIDIA/NeMo-Megatron-Launcher>`_
* `Amazon SageMaker Model Parallel Library <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features-v2-tensor-parallelism.html>`_
* `Levanter <https://github.com/stanford-crfm/levanter>`_
* `GPT-NeoX <https://github.com/EleutherAI/gpt-neox>`_
* `Hugging Face Nanotron <https://github.com/huggingface/nanotron>`_ - Coming soon!
* `Colossal-AI <https://github.com/hpcaitech/ColossalAI>`_ - Coming soon!
* `PeriFlow <https://github.com/friendliai/periflow-python-sdk>`_ - Coming soon!

* `Hugging Face Nanotron <https://github.com/huggingface/nanotron>`_

Contributing
============
Expand Down
Loading