Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Published May 29, 2026 Update on GitHub Upvote 168 Aritra Roy Gosthipaty ariG23498 Sayak Paul sayakpaul Sergio Paniego sergiopaniego Rémi Ouazan Reboul ror Pedro Cuenca pcuenq What you cannot profile, you cannot optimize. Whether you are trying to squeeze more tokens per second out of a Large Language Model (LLM), shave milliseconds off inference, or just understand why your training loop runs slower than the spec sheet promises, the path eventually runs through profiling.

This ProductLaunch is relevant to the technology intelligence record because it involves GitHub, NVIDIA, Hugging Face, Meta. The source article should remain the factual reference for follow-up coverage.

• Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Published May 29, 2026 Update on GitHub Upvote 168 Aritra Roy Gosthipaty ariG23498 Sayak Paul sayakpaul Sergio Paniego sergiopaniego Rémi Ouazan Reboul ror Pedro Cuenca pcuenq What you cannot profile, you cannot optimize.
• Whether you are trying to squeeze more tokens per second out of a Large Language Model (LLM), shave milliseconds off inference, or just understand why your training loop runs slower than the spec sheet promises, the path eventually runs through profiling.
• The catch is that profiling has a steep on-ramp.
• The traces are dense walls of colored rectangles.
• The events carry intimidating names.
• Most tutorials assume you can already read them.