@PyTorch: Zhipeng Wang (@Google) shares technical advances from the @DeepSpeedAI team for large-scale model training at #PyTorchC…
Summary
At PyTorchCon 2026, Zhipeng Wang of Google shared DeepSpeed technical advances for large-scale model training, including AutoSP automatic sequence parallelism, Muon optimizer support, and AutoTP/AutoEP for Hugging Face models, alongside ongoing open-source community collaboration.
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Zhipeng Wang (@Google) shares technical advances from the @DeepSpeedAI team for large-scale model training at #PyTorchCon 2026.
Zhipeng highlights model-systems co-design and optimizations that enable DeepSpeed to deliver high performance across diverse model architectures, workloads, and accelerator platforms. Highlights include AutoSP for automatic sequence parallelism, Muon optimizer support, and AutoTP and AutoEP for Hugging Face models.
He discusses the continued development of the DeepSpeed open source community and collaboration with model developers, hardware vendors, and AI infrastructure partners to expand adoption and enable emerging large-scale AI workloads.
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