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Habana Model Performance Data

See the latest performance data for Gaudi2 training, Gaudi training and Gaudi inference. For information on models and containers that are currently integrated with Habana’s Synapse AI software suite visit the Habana catalog. For more information on future model support, please refer to our SynapseAI roadmap page.

Computer Vision

ResNet Keras

TensorFlow

ResNeXt101

TensorFlow

SSD-ResNet34

TensorFlow

UNet2D

TensorFlow

Mask R-CNN

TensorFlow

DenseNet Keras

TensorFlow

CycleGan

TensorFlow

RetinaNet

TensorFlow

Segnet

TensorFlow

UNet3D

TensorFlow

EfficientDet

TensorFlow

ResNet50, ResNeXt101

PyTorch

Natural Language Processing

Bert

TensorFlow

Albert

TensorFlow

Transformer

TensorFlow

T5-base

TensorFlow

Bert

PyTorch

Recommendation Systems

DLRM

PyTorch

Gaudi2 Reference Models Training Performance

Gaudi Reference Models Training Performance

Gaudi Reference Models Inference Performance


System Configuration:
Gaudi® Platform
HPU: Habana Gaudi Mezzanine cards
System: HLS-1 with eight Gaudi HPU and two Intel® Xeon® Platinum 8280 CPU @ 2.70GHz, and 756GB of System Memory

Gaudi®2 Platform
HPU: Habana Gaudi2 Mezzanine cards
System: HLS-Gaudi2 with eight Gaudi2 HPU and two Intel® Xeon® Platinum 8380 CPU @ 2.30GHz, and 1TB of System Memory

Common Software
Ubuntu20.04, SynapseAI Software version 1.6.0-439
Tensorflow: Models run with Tensorflow v2.9.1 use this Docker image; ones with v2.8.2 use this Docker image
PyTorch: Models run with PyTorch v1.12.0 use this Docker image
Environment: These workloads are run using the Docker images running directly on the Host OS


Performance varies by use, configuration and other factors.  All information provided here is subject to change without notice. Habana Labs may make changes to its test conditions and internal reliability goals at any time.  Contact your Habana Labs representative to obtain the latest Habana Labs product specifications and roadmaps. Your costs and results may vary.

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