README refresh (#7668)

Long overdue

---------

Signed-off-by: Olatunji Ruwase <[email protected]>
Co-authored-by: Stas Bekman <[email protected]>

Author: sfc-gh-truwase

README.md

@@ -15,84 +15,39 @@
 </div>
 
 ## Latest News
-<b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
+
+* [2025/10] [SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips](https://pytorch.org/blog/superoffload-unleashing-the-power-of-large-scale-llm-training-on-superchips/)
+
+* [2025/10] [Study of ZenFlow and ZeRO offload performance with DeepSpeed CPU core binding](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zenflow-corebinding/README.md)
 
 * [2025/08] [ZenFlow: Stall-Free Offloading Engine for LLM Training](https://pytorch.org/blog/zenflow-stall-free-offloading-engine-for-llm-training/)
 
 * [2025/06] [Arctic Long Sequence Training (ALST) with DeepSpeed: Scalable And Efficient Training For Multi-Million Token Sequences](https://www.snowflake.com/en/engineering-blog/arctic-long-sequence-training-multi-million-token-ai/)
 
 * [2025/06] [DeepNVMe: Affordable I/O scaling for Deep Learning Applications](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepnvme/06-2025/README.md)
 
-* [2025/04] [DeepCompile: Unlocking Compiler Optimization for Distributed Training](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepcompile/README.md)
-
-* [2025/03] [DeepSpeed AutoTP: Automatic Tensor Parallel Training of Hugging Face models](https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/huggingface-tp/README.md)
-
 
 <!-- NOTE: we must use html for news items otherwise links will be broken in the 'more news' section -->
 <details>
 <!-- NOTE: Maintain only three items in 'more news' section -->
  <summary>More news</summary>
  <ul>
-   <li>[2024/12] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/ulysses-offload/README.md">Ulysses-Offload: Democratizing Long Context LLM Training</a></li>
-   <li>[2024/12] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md">DeepSpeed-Domino: Communication-Free LLM Training Engine</a></li>
-   <li>[2024/08] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md">DeepSpeed on Windows</a>
-   [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md">日本語</a>]
-   [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/chinese/README.md">中文</a>]</li>
- </ul>
-</details>
-
-
----
-
-# Extreme Speed and Scale for DL Training and Inference
-
-***[DeepSpeed](https://www.deepspeed.ai/) enabled the world's most powerful language models (at the time of this writing) such as [MT-530B](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/) and [BLOOM](https://huggingface.co/blog/bloom-megatron-deepspeed)***. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed you can:
-
-* Train/Inference dense or sparse models with billions or trillions of parameters
-* Achieve excellent system throughput and efficiently scale to thousands of GPUs
-* Train/Inference on resource constrained GPU systems
-* Achieve unprecedented low latency and high throughput for inference
-* Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs
-
----
-
-# DeepSpeed's four innovation pillars
 
-<img src="docs/assets/images/DeepSpeed-pillars.png" width="800px">
+   <li>[2025/04] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepcompile/README.md">DeepCompile: Unlocking Compiler Optimization for Distributed Training</a></li>
 
+   <li>[2025/03] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/huggingface-tp/README.md">DeepSpeed AutoTP: Automatic Tensor Parallel Training of Hugging Face models</a></li>
 
-## DeepSpeed-Training
 
-DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: [DeepSpeed-Training](https://www.deepspeed.ai/training/)
-
-## DeepSpeed-Inference
-
-DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar. Learn more: [DeepSpeed-Inference](https://www.deepspeed.ai/inference)
-
-
-## DeepSpeed-Compression
-
-To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar. Learn more: [DeepSpeed-Compression](https://www.deepspeed.ai/compression)
-
-## DeepSpeed4Science
+   <li>[2024/12] <a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/ulysses-offload/README.md">Ulysses-Offload: Democratizing Long Context LLM Training</a></li>
 
-In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: [tutorials](https://www.deepspeed.ai/deepspeed4science/)
+ </ul>
+</details>
 
 ---
 
-# DeepSpeed Software Suite
-
-## DeepSpeed Library
-
-   The [DeepSpeed](https://github.com/deepspeedai/deepspeed) library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see [DeepSpeed Adoption](#deepspeed-adoption)).
-
-## Model Implementations for Inference (MII)
+# Extreme Speed and Scale for DL Training
 
-   [Model Implementations for Inference (MII)](https://github.com/deepspeedai/deepspeed-mii) is an open-sourced repository for making low-latency and high-throughput inference accessible to all data scientists by alleviating the need to apply complex system optimization techniques themselves. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions.
-
-## DeepSpeed on Azure
-
-   DeepSpeed users are diverse and have access to different environments. We recommend to try DeepSpeed on Azure as it is the simplest and easiest method. The recommended method to try DeepSpeed on Azure is through AzureML [recipes](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/workflows/train/deepspeed). The job submission and data preparation scripts have been made available [here](https://github.com/deepspeedai/Megatron-DeepSpeed/tree/main/examples_deepspeed/azureml). For more details on how to use DeepSpeed on Azure, please follow the [Azure tutorial](https://www.deepspeed.ai/tutorials/azure/).
+***[DeepSpeed](https://www.deepspeed.ai/) enabled the world's most powerful language models (at the time of this writing) such as [MT-530B](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/) and [BLOOM](https://huggingface.co/blog/bloom-megatron-deepspeed)***. DeepSpeed offers a confluence of [system innovations](https://www.deepspeed.ai/training/), that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations include ZeRO, ZeRO-Infinity, 3D-Parallelism, Ulysses Sequence Parallelism, DeepSpeed-MoE, etc.
 
 ---
 
@@ -197,9 +152,6 @@ Many DeepSpeed features are supported on Windows for both training and inference
 3. Launch Cmd console with Administrator permissions for creating required symlink folders and ensure MSVC tools are added to your PATH or launch the Developer Command Prompt for Visual Studio 2022 with administrator permissions.
 4. Run `build_win.bat` to build wheel in `dist` folder.
 
-# Features
-
-Please checkout [DeepSpeed-Training](https://www.deepspeed.ai/training), [DeepSpeed-Inference](https://www.deepspeed.ai/inference) and [DeepSpeed-Compression](https://www.deepspeed.ai/compression) pages for full set of features offered along each of these three pillars.
 
 # Further Reading
 
@@ -284,7 +236,7 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 33. Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training [arXiv:2406.18820](https://arxiv.org/abs/2406.18820)
 34. Stas Bekman, Samyam Rajbhandari, Michael Wyatt, Jeff Rasley, Tunji Ruwase, Zhewei Yao, Aurick Qiao, Yuxiong He. (2025) Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences [arXiv:2506.13996](https://arxiv.org/abs/2506.13996)
 35. Tingfeng Lan, Yusen Wu, Bin Ma, Zhaoyuan Su, Rui Yang, Tekin Bicer, Masahiro Tanaka, Olatunji Ruwase, Dong Li, Yue Cheng. (2025) ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates [arXiv:2505.12242](https://arxiv.org/abs/2505.12242)
-36. Xinyu Lian, Masahiro Tanaka, Olatunji Ruwase, Minjia Zhang. (2026) SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips [ASPLOS 2026](https://www.asplos-conference.org/asplos2026)
+36. Xinyu Lian, Masahiro Tanaka, Olatunji Ruwase, Minjia Zhang. (2026) SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips [arxiv](https://arxiv.org/abs/2509.21271), [ASPLOS 2026](https://www.asplos-conference.org/asplos2026)
 
 # Videos
 1. DeepSpeed KDD 2020 Tutorial