fix(readme): update broken links and modernize project description

README.rst

@@ -38,21 +38,19 @@ precision-like API that can be used seamlessly with your framework-specific code
 framework agnostic C++ API that can be integrated with other deep learning libraries to enable FP8
 support for Transformers.
 
-As the number of parameters in Transformer models continues to grow, training and inference for
-architectures such as BERT, GPT and T5 become very memory and compute-intensive. Most deep learning
-frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for
-many deep learning models. Using mixed-precision training, which combines single-precision (FP32)
-with lower precision (e.g. FP16) format when training a model, results in significant speedups with
-minimal differences in accuracy as compared to FP32 training. With Hopper GPU
-architecture FP8 precision was introduced, which offers improved performance over FP16 with no
-degradation in accuracy. Although all major deep learning frameworks support FP16, FP8 support is
-not available natively in frameworks today.
-
-TE addresses the problem of FP8 support by providing APIs that integrate with popular Large Language
-Model (LLM) libraries. It provides a Python API consisting of modules to easily build a Transformer
-layer as well as a framework-agnostic library in C++ including structs and kernels needed for FP8
-support. Modules provided by TE internally maintain scaling factors and other values needed for FP8
-training, greatly simplifying mixed precision training for users.
+As Transformer models scale to hundreds of billions of parameters across large language models,
+MoE architectures, and multimodal models, training and inference become increasingly
+memory and compute-intensive. Mixed-precision training, which combines single-precision (FP32) with
+lower precision formats, delivers significant speedups with minimal impact on accuracy. FP8, introduced
+with the Hopper GPU architecture, offers further performance gains over FP16 with no degradation in
+accuracy, and newer formats like MXFP8 and NVFP4 on Blackwell push efficiency even further.
+
+TE integrates with popular LLM frameworks and provides optimizations that make low-precision training
+work seamlessly with advanced features like MoE, tensor/sequence/context parallelism, and fused
+operations. It provides a Python API consisting of modules to easily build a Transformer layer as
+well as a framework-agnostic library in C++ including structs and kernels needed for FP8 support.
+Modules provided by TE internally maintain scaling factors and other values needed for FP8 training,
+greatly simplifying mixed precision training for users.
 
 Highlights
 ==========
@@ -139,8 +137,8 @@ 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 `Getting Started Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/getting_started.html>`_.
+For a more comprehensive tutorial, check out our `Getting Started Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/getting_started/index.html>`_.
+For a more comprehensive tutorial, check out our `Getting Started Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/index.html>`_.
 
 .. overview-end-marker-do-not-remove
 
@@ -383,7 +381,7 @@ FP8 and MXFP8 have been tested extensively across different model architectures
 +------------+------------------+---------------------------------------------------------------------------------------------------------+
 | Model      | Framework        | Source                                                                                                  |
 +============+==================+=========================================================================================================+
-| MPT-1.3B   |  Mosaic Composer | https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1                                              |
+| MPT-1.3B   |  Mosaic Composer | https://www.databricks.com/blog/coreweave-nvidia-h100-part-1                                              |
 +------------+------------------+---------------------------------------------------------------------------------------------------------+
 | LLama2-7B  |  Alibaba Pai     | https://mp.weixin.qq.com/s/NQT0uKXLbXyh5031zBdeBQ                                                       |
 +------------+------------------+---------------------------------------------------------------------------------------------------------+
@@ -471,8 +469,8 @@ Previous News
   :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] `Unleashing The Power Of Transformers With NVIDIA Transformer Engine <https://lambda.ai/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>`_
+* [04/2023] `Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1) <https://www.databricks.com/blog/coreweave-nvidia-h100-part-1>`_