# Measure Machine Learning Inference Performance on Arm servers

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ml-perf/)
- [Measure ML Inference Performance on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ml-perf/ml-perf/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ml-perf/_next-steps/)

## About this Learning Path

| Skill level:      | Introductory       |
|-------------------|--------------------|
| Reading time:     | 20 min             |
| Last updated:     | 31 Jul 2026        |

| Author:          | Pareena Verma, Arm  [GitHub](https://github.com/pareenaverma) [LinkedIn](https://linkedin.com/in/pareena-verma-7853607) |
|-------------------|--------------------|
| Arm IP:          | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags:            | [ML](https://learn.arm.com/tag/ml), [AWS](https://learn.arm.com/tag/aws), [Oracle](https://learn.arm.com/tag/oracle), [Linux](https://learn.arm.com/tag/linux), [TensorFlow](https://learn.arm.com/tag/tensorflow), [Runbook](https://learn.arm.com/tag/runbook) |

### Who is this for?
This is an introductory topic for software developers interested in benchmarking machine learning workloads on Arm servers.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Install and run TensorFlow on your Arm-based cloud server
- Use MLPerf Inference benchmark suite, an open-sourced benchmark from MLCommons to test ML performance on your Arm server

### Prerequisites
Before starting, you will need the following:
- An [Arm based instance](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/) from an appropriate cloud service provider or an on-premise Arm server.