Building and Benchmarking DLRM on Arm Neoverse V2 with MLPerf
Introduction
Overview and setup
Download model weights and data
Run the benchmark
Next Steps
Building and Benchmarking DLRM on Arm Neoverse V2 with MLPerf
Who is this for?
This is an introductory topic for software developers who want to set up a pipeline in the cloud for recommendation models. You'll build and run the Deep Learning Recommendation Model (DLRM) and benchmark its performance using MLPerf and PyTorch.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Build the Deep Learning Recommendation Model (DLRM).
- Run a modified performant DLRMv2 benchmark and inspect the results.
Prerequisites
Before starting, you will need the following:
- Any Arm-based instance from a cloud service provider (CSP), or an on-premise Arm server with at least 400GB of RAM and 800 GB of disk space.
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
rclone, and run a modified MLPerf DLRM benchmark. You’ll create dedicated directories, clone the provided repository, use PyTorch 2.9.0+cpu with Arm-focused optimizations, and execute the benchmark. You’ll then inspect the output to confirm a successful run and review the results.Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
rclone installed successfully, run rclone config before downloading the data and model weights.$HOME/data and $HOME/model. Download the dataset into data and the model weights into model.