# [Measure and accelerate PyTorch Inference on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/torchbench/)

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/torchbench/)
- [Measure and accelerate the inference performance of PyTorch models on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/torchbench/pytorch-benchmark/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/torchbench/_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) [Microsoft Azure](https://learn.arm.com/tag/microsoft-azure) [Google Cloud](https://learn.arm.com/tag/google-cloud) [Oracle](https://learn.arm.com/tag/oracle) [Linux](https://learn.arm.com/tag/linux) [Python](https://learn.arm.com/tag/python) [PyTorch](https://learn.arm.com/tag/pytorch) |

### Who is this for?
This is an introductory topic for software developers who want to learn how to measure and accelerate the performance of Natural Language Processing (NLP), vision and recommender PyTorch models on Arm-based servers.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Download and install the PyTorch Benchmarks suite.
- Evaluate PyTorch model inference performance on an Arm-based server using the PyTorch Benchmark suite.
- Compare the model inference performance using eager mode and `torch.compile` mode in PyTorch.

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