---
type: "post"
id: "profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b"
slug: "profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b"
title: "Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium."
description: "Hugging Face and AWS published the first part of a tutorial on system-level profiling for TorchNeuron, the native PyTorch backend for AWS Trainium."
retrieval_nugget: "Hugging Face and AWS published the first part of a tutorial on system-level profiling for TorchNeuron, the native PyTorch backend for AWS Trainium."
published_at: "2026-10-21"
updated_at: "2026-10-21"
record_date: "2026-10-21"
date_kind: "published_at"
topics: ["observability","inference","compute"]
entities: ["AWS","Hugging Face","AWS Trainium","TorchNeuron","PyTorch","Neuron Explorer","Neuron Kernel Interface"]
related_patterns: ["runtime-observability-becomes-the-primary-engineering-focus-for-ai"]
novelty_label: "new_fact"
what_changed: "Adds a hands-on guide to profiling TorchNeuron on Trainium, tracing operations from PyTorch dispatch through runtime scheduling to NeuronCore execution across three implementations."
prior_refs: ["memory:b442cbe8-53c1-8af1-a5cb-9b69a67f000b","memory:56b3a5c0-0a88-82ec-a009-9198f84a4252"]
source_url: "https://huggingface.co/blog/amazon/profiling-torchneuron-part-1"
source_urls: ["https://huggingface.co/blog/amazon/profiling-torchneuron-part-1"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
routes: {"html":"https://newruntime.com/posts/profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b/","markdown":"https://newruntime.com/posts/profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b.md","json":"https://newruntime.com/posts/profiling-torchneuron-part-1-system-level-profiling-on-6f6b9b.json"}
---

# Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.

## Retrieval answer

Hugging Face and AWS published the first part of a tutorial on system-level profiling for TorchNeuron, the native PyTorch backend for AWS Trainium.

Hugging Face and AWS published the first part of a tutorial on system-level profiling for TorchNeuron, the native PyTorch backend for AWS Trainium. The walkthrough configures `torch.profiler` with `ProfilerActivity.PrivateUse1`, `NeuronConfig`, and a Neuron trace export callback, then inspects the resulting data in Neuron Explorer.

The example profiles three implementations of RMSNorm using the same inputs and profiling schedule. A naive version made from individual PyTorch operations recorded 2.355 ms of Neuron device time across six operator calls. `torch.rms_norm` lowered to one fused Neuron kernel and recorded 403.3 microseconds. A naive function compiled with `torch.compile(backend="neuron")` ran as one compiled region at 375.5 microseconds.

The post also describes trace artifacts: a compiled Neuron executable is stored in a NEFF file, while its hardware instruction trace is stored in a paired NTFF file. Neuron Explorer can display framework dispatch, runtime scheduling, and NeuronCore execution.

The source does not establish performance for complete models, other operators, or other Trainium configurations. Its figures are measurements from this RMSNorm example, not a general Trainium benchmark.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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