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Title: GitHub - deepspeedai/DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - deepspeedai/DeepSpeed

Open Graph Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - deepspeedai/DeepSpeed

X Description: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - deepspeedai/DeepSpeed

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DeepSpeed Core API updates: PyTorch-style backward and low-precision master stateshttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/core_api_update/README.md
Ray x DeepSpeed Meetuphttps://luma.com/3wctqteh
herehttps://docs.google.com/presentation/d/1eM3mY6oW9GYkRy1Xz0iOnbbEr5T1t0JJXOM5BKtR-Ks/edit?slide=id.g38615d6b4c2_0_87#slide=id.g38615d6b4c2_0_87
SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchipshttps://pytorch.org/blog/superoffload-unleashing-the-power-of-large-scale-llm-training-on-superchips/
Study of ZenFlow and ZeRO offload performance with DeepSpeed CPU core bindinghttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zenflow-corebinding/README.md
ZenFlow: Stall-Free Offloading Engine for LLM Traininghttps://pytorch.org/blog/zenflow-stall-free-offloading-engine-for-llm-training/
Arctic Long Sequence Training (ALST) with DeepSpeed: Scalable And Efficient Training For Multi-Million Token Sequenceshttps://www.snowflake.com/en/engineering-blog/arctic-long-sequence-training-multi-million-token-ai/
DeepNVMe: Affordable I/O scaling for Deep Learning Applicationshttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepnvme/06-2025/README.md
DeepCompile: Unlocking Compiler Optimization for Distributed Traininghttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepcompile/README.md
DeepSpeed AutoTP: Automatic Tensor Parallel Training of Hugging Face modelshttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/huggingface-tp/README.md
Ulysses-Offload: Democratizing Long Context LLM Traininghttps://github.com/deepspeedai/DeepSpeed/blob/master/blogs/ulysses-offload/README.md
https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed#extreme-speed-and-scale-for-dl-training
DeepSpeedhttps://www.deepspeed.ai/
MT-530Bhttps://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/
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https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed#deepspeed-adoption
AI at Scalehttps://www.microsoft.com/en-us/research/project/ai-at-scale/
herehttps://innovation.microsoft.com/en-us/exploring-ai-at-scale
Megatron-Turing NLG (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/
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GLM (130B)https://github.com/THUDM/GLM-130B
xTrimoPGLM (100B)https://www.biorxiv.org/content/10.1101/2023.07.05.547496v2
YaLM (100B)https://github.com/yandex/YaLM-100B
GPT-NeoX (20B)https://github.com/EleutherAI/gpt-neox
AlexaTM (20B)https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning
Turing NLG (17B)https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/
METRO-LM (5.4B)https://arxiv.org/pdf/2204.06644.pdf
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https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed#publications
arXiv:1910.02054https://arxiv.org/abs/1910.02054
In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20)https://dl.acm.org/doi/10.5555/3433701.3433727
In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20, Tutorial)https://dl.acm.org/doi/10.1145/3394486.3406703
arXiv:2010.13369https://arxiv.org/abs/2010.13369
NeurIPS 2020https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html
arXiv:2101.06840https://arxiv.org/abs/2101.06840
USENIX ATC 2021https://www.usenix.org/conference/atc21/presentation/ren-jie
[paper]https://arxiv.org/abs/2101.06840
[slides]https://www.usenix.org/system/files/atc21_slides_ren-jie.pdf
[blog]https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/
arXiv:2102.02888https://arxiv.org/abs/2102.02888
ICML 2021http://proceedings.mlr.press/v139/tang21a.html
arXiv:2104.07857https://arxiv.org/abs/2104.07857
SC 2021https://dl.acm.org/doi/abs/10.1145/3458817.3476205
[paper]https://arxiv.org/abs/2104.07857
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/SC21-ZeRO-Infinity.pdf
[blog]https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/
arXiv:2104.06069https://arxiv.org/abs/2104.06069
HiPC 2022https://hipc.org/advance-program/
arXiv:2108.06084https://arxiv.org/abs/2108.06084
NeurIPS 2022https://openreview.net/forum?id=JpZ5du_Kdh
arXiv:2202.06009https://arxiv.org/abs/2202.06009
arXiv:2201.05596https://arxiv.org/abs/2201.05596
ICML 2022https://proceedings.mlr.press/v162/rajbhandari22a.html
[pdf]https://arxiv.org/abs/2201.05596
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/ICML-5mins.pdf
[blog]https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/
arXiv:2201.11990https://arxiv.org/abs/2201.11990
arXiv:2206.01859https://arxiv.org/abs/2206.01859
NeurIPS 2022https://openreview.net/forum?id=xNeAhc2CNAl
arXiv:2206.01861https://arxiv.org/abs/2206.01861
NeurIPS 2022https://openreview.net/forum?id=f-fVCElZ-G1
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
[blog]https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/
arXiv:2207.00032https://arxiv.org/abs/2207.00032
SC 2022https://dl.acm.org/doi/abs/10.5555/3571885.3571946
[paper]https://arxiv.org/abs/2207.00032
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/sc22-ds-inference.pdf
[blog]https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/
arXiv:2211.11586https://arxiv.org/abs/2211.11586
arXiv:2212.03597https://arxiv.org/abs/2212.03597
ENLSP2023 Workshop at NeurIPS2023https://neurips2023-enlsp.github.io/
arXiv:2301.12017https://arxiv.org/abs/2301.12017
ICML2023https://icml.cc/Conferences/2023
ICLR:2023https://openreview.net/forum?id=Pgtn4l6eKjv
arXiv:2303.07226https://arxiv.org/abs/2303.07226
Finding at EMNLP2023https://2023.emnlp.org/
arXiv:2303.08374https://arxiv.org/abs/2303.08374
arXiv:2303.06318https://arxiv.org/abs/2303.06318
ICS 2023https://dl.acm.org/doi/10.1145/3577193.3593704
arXiv:2306.10209https://arxiv.org/abs/2306.10209
ML for Sys Workshop at NeurIPS2023http://mlforsystems.org/
[blog]https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/
arXiv:2303.08302https://arxiv.org/abs/2303.08302
ENLSP2023 Workshop at NeurIPS2023https://neurips2023-enlsp.github.io/
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
arXiv:2305.09847https://arxiv.org/abs/2305.09847
arXiv:2308.01320https://arxiv.org/abs/2308.01320
arXiv:2307.09782https://arxiv.org/abs/2307.09782
ENLSP2023 Workshop at NeurIPS2023https://neurips2023-enlsp.github.io/
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf
arXiv:2309.14327https://arxiv.org/pdf/2309.14327.pdf
arXiv:2310.04610https://arxiv.org/abs/2310.04610
[blog]https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/
arXiv:2310.17723https://arxiv.org/abs/2310.17723
arXiv:2312.08583https://arxiv.org/abs/2312.08583
arXiv:2401.14112https://arxiv.org/abs/2401.14112
System Optimizations for Enabling Training of Extreme Long Sequence Transformer Modelshttps://dl.acm.org/doi/10.1145/3662158.3662806
arXiv:2406.18820https://arxiv.org/abs/2406.18820
arXiv:2506.13996https://arxiv.org/abs/2506.13996
arXiv:2505.12242https://arxiv.org/abs/2505.12242
arxivhttps://arxiv.org/abs/2509.21271
ASPLOS 2026https://www.asplos-conference.org/asplos2026
https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed#videos
Overviewhttps://www.youtube.com/watch?v=CaseqC45DNc&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=29
ZeRO + large model traininghttps://www.youtube.com/watch?v=y4_bCiAsIAk&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=28
17B T-NLG demohttps://www.youtube.com/watch?v=9V-ZbP92drg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=27
Fastest BERT training + RScan tuninghttps://www.youtube.com/watch?v=o1K-ZG9F6u0&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=26
part 1https://www.youtube.com/watch?v=_NOk-mBwDYg&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=92
part 2https://www.youtube.com/watch?v=sG6_c4VXLww&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=94
part 3https://www.youtube.com/watch?v=k9yPkBTayos&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=93
FAQhttps://www.youtube.com/watch?v=nsHu6vEgPew&list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&index=24
ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeedhttps://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html
DeepSpeed on AzureMLhttps://youtu.be/yBVXR8G8Bg8
Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conferencehttps://www.youtube.com/watch?v=cntxC3g22oU
[slides]https://patch-diff.githubusercontent.com/deepspeedai/DeepSpeed/blob/master/docs/assets/files/presentation-mlops.pdf
DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)https://www.youtube.com/watch?v=pDGI668pNg0
Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)https://www.youtube.com/watch?v=tC01FRB0M7w
Ultimate Guide To Scaling ML Models (The AI Epiphany)https://www.youtube.com/watch?v=hc0u4avAkuM
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