Optimized Intel® Reference Models for Intel® Data Center GPU Max Series

ID 814742
Updated 3/18/2024
Version Latest




This document provides links to step-by-step instructions on how to leverage reference model docker containers to run optimized open-source Deep Learning training and inference workloads using Intel® Extension for PyTorch* and Intel® Extension for TensorFlow* on the Intel® Data Center GPU Max Series.

Base Containers

AI Framework Extension Documentation
PyTorch Intel® Extension for PyTorch* Intel® Extension for PyTorch Container
TensorFlow Intel® Extension for TensorFlow* Intel® Extension for TensorFlow Container


Optimized Workloads

The table below provides links to run each workload in a docker container. The containers were validated on a host running Linux*.

Model Framework Mode Precisions
3D-UNet TensorFlow Training BF16
BERT Large PyTorch Inference FP16, BF16 and FP32
BERT Large PyTorch Training BF16,TF32 and FP32
BERT Large TensorFlow Training BF16
DistilBERT PyTorch Inference FP16,BF16 and FP32
DLRM PyTorch Inference FP16
DLRM PyTorch Training FP32,TF32 and BF16
Mask R-CNN TensorFlow Training BF16
ResNet50 v1.5 PyTorch Inference INT8,FP16,BF16,FP32 and TF32
ResNet50 v1.5 PyTorch Training BF16,FP32 and TF32
ResNet50 v1.5 TensorFlow Training BF16
RNN-T PyTorch Inference FP16,BF16 and FP32
RNN-T PyTorch Training BF16,TF32 and FP32
Stable Diffusion PyTorch Inference FP16



  • DLRM(PyTorch) inference workload is supported on older Intel® Extension for TensorFlow* v2.13 and Intel® Extension for PyTorch* 2.0.110+xpu versions.
  • The other models in the list are validated on Intel® Extension for TensorFlow* v2.14 and Intel® Extension for PyTorch* 2.1.10+xpu versions.