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Title: GitHub - philipphack/TransformerEngine: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

Open Graph Title: GitHub - philipphack/TransformerEngine: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

X Title: GitHub - philipphack/TransformerEngine: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

Description: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference. - philipphack/TransformerEngine

Open Graph Description: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilizatio...

X Description: A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilizatio...

Opengraph URL: https://github.com/philipphack/TransformerEngine

X: @github

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https://github.com/philipphack/TransformerEngine#latest-news
Turbocharged Training: Optimizing the Databricks Mosaic AI stack with FP8https://www.databricks.com/blog/turbocharged-training-optimizing-databricks-mosaic-ai-stack-fp8
FP8 Training Support in SageMaker Model Parallelism Libraryhttps://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-release-notes.html
New NVIDIA NeMo Framework Features and NVIDIA H200https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/
https://github.com/philipphack/TransformerEngine/blob/main/docs/examples/H200-NeMo-performance.png
Inflection-2: The Next Step Uphttps://inflection.ai/inflection-2
Unleashing The Power Of Transformers With NVIDIA Transformer Enginehttps://lambdalabs.com/blog/unleashing-the-power-of-transformers-with-nvidia-transformer-engine
Accelerating PyTorch Training Workloads with FP8https://towardsdatascience.com/accelerating-pytorch-training-workloads-with-fp8-5a5123aec7d7
Transformer Engine added to AWS DL Container for PyTorch Traininghttps://github.com/aws/deep-learning-containers/pull/3315
Breaking MLPerf Training Records with NVIDIA H100 GPUshttps://developer.nvidia.com/blog/breaking-mlperf-training-records-with-nvidia-h100-gpus/
Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1)https://www.mosaicml.com/blog/coreweave-nvidia-h100-part-1
https://github.com/philipphack/TransformerEngine#what-is-transformer-engine
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https://github.com/philipphack/TransformerEngine#fp8-convergence
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https://github.com/philipphack/TransformerEngine#contributing
CONTRIBUTING.rsthttps://github.com/philipphack/TransformerEngine/blob/main/CONTRIBUTING.rst
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Attention original paperhttps://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Megatron-LM tensor parallelhttps://arxiv.org/pdf/1909.08053.pdf
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FP8 Formats for Deep Learninghttps://arxiv.org/abs/2209.05433
https://github.com/philipphack/TransformerEngine#videos
What's New in Transformer Engine and FP8 Training | GTC 2024https://www.nvidia.com/en-us/on-demand/session/gtc24-s62457/
FP8 Training with Transformer Engine | GTC 2023https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51393
FP8 for Deep Learning | GTC 2023https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s52166/
Inside the Hopper Architecturehttps://www.nvidia.com/en-us/on-demand/session/gtcspring22-s42663/
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