Update README: Added GTC 2025 videos, latest news, and improved doc links (#1752)

* added a direct link to the quickstart notebook right after the code examples section
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* updated link in README for HF Accelerate docs
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* update DeepSpeed integration link
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Update Release Notes link to documentation archive
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* updated latest news and moved older news under a dropdown caret
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* moved previous news to bottom of readme
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fixed previous news link
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* added gtc videos
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* added TE GTC 2025 talk to latest news
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: Santosh Bhavani <sbhavani@nvidia.com>

README.rst

@@ -8,25 +8,22 @@
 Transformer Engine
 ==================
 
-`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/release-notes/index.html>`_
+`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>`_
 
 Latest News
 ===========
+* [03/2025] `Stable and Scalable FP8 Deep Learning Training on Blackwell | GTC 2025 <https://www.nvidia.com/en-us/on-demand/session/gtc25-s72778/>`_
+* [03/2025] `Measure and Improve AI Workload Performance with NVIDIA DGX Cloud Benchmarking <https://developer.nvidia.com/blog/measure-and-improve-ai-workload-performance-with-nvidia-dgx-cloud-benchmarking/>`_
 
-* [03/2024] `Turbocharged Training: Optimizing the Databricks Mosaic AI stack with FP8 <https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8>`_
-* [03/2024] `FP8 Training Support in SageMaker Model Parallelism Library <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-release-notes.html>`_
-* [12/2023] `New NVIDIA NeMo Framework Features and NVIDIA H200 <https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/>`_
-
-.. image:: docs/examples/H200-NeMo-performance.png
+.. image:: docs/examples/comparison-fp8-bf16-training-nvidia-dgx-cloud-benchmarking-performance-explorer.jpg
   :width: 600
-  :alt: H200
+  :alt: Comparison of FP8 versus BF16 training, as seen in NVIDIA DGX Cloud Benchmarking Performance Explorer
 
-* [11/2023] `Inflection-2: The Next Step Up <https://inflection.ai/inflection-2>`_
-* [11/2023] `Unleashing The Power Of Transformers With NVIDIA Transformer Engine <https://lambdalabs.com/blog/unleashing-the-power-of-transformers-with-nvidia-transformer-engine>`_
-* [11/2023] `Accelerating PyTorch Training Workloads with FP8 <https://towardsdatascience.com/accelerating-pytorch-training-workloads-with-fp8-5a5123aec7d7>`_
-* [09/2023] `Transformer Engine added to AWS DL Container for PyTorch Training <https://github.com/aws/deep-learning-containers/pull/3315>`_
-* [06/2023] `Breaking MLPerf Training Records with NVIDIA H100 GPUs <https://developer.nvidia.com/blog/breaking-mlperf-training-records-with-nvidia-h100-gpus/>`_
-* [04/2023] `Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1) <https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1>`_
+* [02/2025] `Understanding the Language of Life's Biomolecules Across Evolution at a New Scale with Evo 2 <https://developer.nvidia.com/blog/understanding-the-language-of-lifes-biomolecules-across-evolution-at-a-new-scale-with-evo-2/>`_
+* [02/2025] `NVIDIA DGX Cloud Introduces Ready-To-Use Templates to Benchmark AI Platform Performance <https://developer.nvidia.com/blog/nvidia-dgx-cloud-introduces-ready-to-use-templates-to-benchmark-ai-platform-performance/>`_
+* [01/2025] `Continued Pretraining of State-of-the-Art LLMs for Sovereign AI and Regulated Industries with iGenius and NVIDIA DGX Cloud <https://developer.nvidia.com/blog/continued-pretraining-of-state-of-the-art-llms-for-sovereign-ai-and-regulated-industries-with-igenius-and-nvidia-dgx-cloud/>`_
+
+`Previous News <#previous-news>`_
 
 What is Transformer Engine?
 ===========================
@@ -141,6 +138,8 @@ Flax
       for _ in range(10):
         loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)
 
+For a more comprehensive tutorial, check out our `Quickstart Notebook <https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb>`_.
+
 .. overview-end-marker-do-not-remove
 
 Installation
@@ -349,8 +348,8 @@ Integrations
 
 Transformer Engine has been integrated with popular LLM frameworks such as:
 
-* `DeepSpeed <https://github.com/microsoft/DeepSpeed/pull/3731>`_
-* `Hugging Face Accelerate <https://github.com/huggingface/accelerate/releases/tag/v0.17.0>`_
+* `DeepSpeed <https://github.com/deepspeedai/DeepSpeed/blob/master/tests/unit/runtime/half_precision/test_fp8.py>`_
+* `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>`_
 * `MosaicML Composer <https://github.com/mosaicml/composer/releases/tag/v0.13.1>`_
 * `NVIDIA JAX Toolbox <https://github.com/NVIDIA/JAX-Toolbox>`_
@@ -381,10 +380,37 @@ Papers
 Videos
 ======
 
+* `Stable and Scalable FP8 Deep Learning Training on Blackwell | GTC 2025 <https://www.nvidia.com/en-us/on-demand/session/gtc24-s62457/>`_
+* `Blackwell Numerics for AI | GTC 2025 <https://www.nvidia.com/en-us/on-demand/session/gtc25-s72458/>`_
+* `Building LLMs: Accelerating Pretraining of Foundational Models With FP8 Precision | GTC 2025 <https://www.nvidia.com/gtc/session-catalog/?regcode=no-ncid&ncid=no-ncid&tab.catalogallsessionstab=16566177511100015Kus&search=zoho#/session/1726152813607001vnYK>`_
+* `From FP8 LLM Training to Inference: Language AI at Scale | GTC 2025 <https://www.nvidia.com/en-us/on-demand/session/gtc25-s72799/>`_
 * `What's New in Transformer Engine and FP8 Training | GTC 2024 <https://www.nvidia.com/en-us/on-demand/session/gtc24-s62457/>`_
 * `FP8 Training with Transformer Engine | GTC 2023 <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51393>`_
 * `FP8 for Deep Learning | GTC 2023 <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s52166/>`_
-* `Inside the Hopper Architecture <https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42663/>`_
+* `Inside the Hopper Architecture | GTC 2022 <https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42663/>`_
 
 .. |License| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
    :target: https://opensource.org/licenses/Apache-2.0
+
+Previous News
+=============
+
+* [11/2024] `Developing a 172B LLM with Strong Japanese Capabilities Using NVIDIA Megatron-LM <https://developer.nvidia.com/blog/developing-a-172b-llm-with-strong-japanese-capabilities-using-nvidia-megatron-lm/>`_
+* [11/2024] `How FP8 boosts LLM training by 18% on Amazon SageMaker P5 instances <https://aws.amazon.com/blogs/machine-learning/how-fp8-boosts-llm-training-by-18-on-amazon-sagemaker-p5-instances/>`_
+* [11/2024] `Efficiently train models with large sequence lengths using Amazon SageMaker model parallel <https://aws.amazon.com/blogs/machine-learning/efficiently-train-models-with-large-sequence-lengths-using-amazon-sagemaker-model-parallel/>`_
+* [09/2024] `Reducing AI large model training costs by 30% requires just a single line of code from FP8 mixed precision training upgrades <https://company.hpc-ai.com/blog/reducing-ai-large-model-training-costs-by-30-requires-just-a-single-line-of-code-from-fp8-mixed-precision-training-upgrades>`_
+* [05/2024] `Accelerating Transformers with NVIDIA cuDNN 9 <https://developer.nvidia.com/blog/accelerating-transformers-with-nvidia-cudnn-9/>`_
+* [03/2024] `Turbocharged Training: Optimizing the Databricks Mosaic AI stack with FP8 <https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8>`_
+* [03/2024] `FP8 Training Support in SageMaker Model Parallelism Library <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-release-notes.html>`_
+* [12/2023] `New NVIDIA NeMo Framework Features and NVIDIA H200 <https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/>`_
+
+.. image:: docs/examples/H200-NeMo-performance.png
+  :width: 600
+  :alt: H200
+
+* [11/2023] `Inflection-2: The Next Step Up <https://inflection.ai/inflection-2>`_
+* [11/2023] `Unleashing The Power Of Transformers With NVIDIA Transformer Engine <https://lambdalabs.com/blog/unleashing-the-power-of-transformers-with-nvidia-transformer-engine>`_
+* [11/2023] `Accelerating PyTorch Training Workloads with FP8 <https://towardsdatascience.com/accelerating-pytorch-training-workloads-with-fp8-5a5123aec7d7>`_
+* [09/2023] `Transformer Engine added to AWS DL Container for PyTorch Training <https://github.com/aws/deep-learning-containers/pull/3315>`_
+* [06/2023] `Breaking MLPerf Training Records with NVIDIA H100 GPUs <https://developer.nvidia.com/blog/breaking-mlperf-training-records-with-nvidia-h100-gpus/>`_
+* [04/2023] `Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1) <https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1>`_