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docs(readme): update convergence table, latest news, and outdated links #2638

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sbhavani Feb 1, 2026
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sbhavani Feb 2, 2026
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33 changes: 13 additions & 20 deletions README.rst
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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 @@ -91,6 +94,7 @@ PyTorch
loss = out.sum()
loss.backward()

For a tutorial with more details, see the `Quickstart Notebook <https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb>`_.
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The referenced quickstart.ipynb file does not exist in docs/examples/. The actual notebooks in that directory are fp8_primer.ipynb, advanced_optimizations.ipynb, and te_jax_integration.ipynb. Consider using one of these existing notebooks or creating the quickstart notebook before merging.


JAX
^^^
Expand Down Expand Up @@ -175,15 +179,15 @@ For example to use the NGC PyTorch container interactively,

.. code-block:: bash

docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:25.08-py3
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:26.01-py3

For example to use the NGC JAX container interactively,

.. code-block:: bash

docker run --gpus all -it --rm nvcr.io/nvidia/jax:25.08-py3
docker run --gpus all -it --rm nvcr.io/nvidia/jax:26.01-py3

Where 25.08 (corresponding to August 2025 release) is the container version.
Where 26.01 (corresponding to January 2026 release) is the container version.

**Benefits of using NGC containers:**

Expand Down Expand Up @@ -343,46 +347,35 @@ 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 |
+------------+------------------+---------------------------------------------------------------------------------------------------------+
| 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 |
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extra whitespace in "Megatron Core" framework names - inconsistent with other rows

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

Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!

+------------+------------------+---------------------------------------------------------------------------------------------------------+
| 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
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