---
title: "Profiling"
description: "1 posts about Profiling, summarised, each linking to the original."
---

# Profiling
> 1 posts about Profiling, summarised, each linking to the original.

## Articles

### [Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.](https://yomu.fyi/post/profiling-torchneuron-part-1-system-level-profiling-on-aws-trainium.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Josh Longenecker
- Published: Oct 21, 2026
- Type: Tutorial

The post introduces system-level profiling of inference performance on AWS Trainium using TorchNeuron. It explains how to configure torch.profiler with the Neuron backend by passing a NeuronConfig and using NeuronProfiler to capture traces. The walkthrough profiles three implementations of RMSNorm: naive PyTorch operations, the fused torch.rms\_norm, and naive operations compiled with torch.compile. The key\_averages() table shows that the naive implementation spends 2,355 µs on the Neuron device across six operator dispatches, while both fused and compiled versions reduce device time to about 400 µs with a single dispatch. Neuron Explorer visually confirms the lifecycle of each operation from framework dispatch through runtime scheduling to hardware execution, revealing the gaps that fusion eliminates.