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Our Poplar SDK accelerates machine learning training and inference with high-performance optimisations delivering world leading performance on IPUs across models such as  natural language processing,  probabilistic modelling, computer vision and more. We have provided a selection of the latest MK2 IPU performance benchmark charts on this page and will update it regularly. We also now provide detailed MK2 training and inference performance data in table format. You can reproduce all of these benchmarks using code in the examples repo on the Graphcore GitHub page. 

MK1 Benchmarks View Performance Results Table

BERT Large (Bidirectional Encoder Representations from Transformers) is one of the most well known NLP models in use today. The IPU accelerates both training and inference on BERT-Large, delivering faster time to train with significantly higher throughput at extremely low latency for inference. 

Bert-Large- Inference Click to Zoom
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Bert-Large: Training Click to Zoom
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IPU excels with models designed to leverage small group convolutions due to its fine grained architecture and unique Poplar features. We deliver unparalleled performance for both training and inference for newer computer vision models like EfficientNet and ResNeXt, which deliver higher accuracy and improved efficiency, as well as for traditional computer vision models such as ResNet-50.

EfficientNet-BO- Inference Click to Zoom
EfficientNet B4- Training Click to Zoom
Resnet 50- Inference Click to Zoom
Resnet50- Training Click to Zoom
MCMC Probabalistic Model- Training Click to Zoom
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DeepVoice 3 TTS- Training Click to Zoom
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