Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets --[0--> --]--> Back to Articles Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Published May 29, 2026 Update on GitHub Upvote 31 +25 Aritra Roy Gosthipaty ariG23498 Follow Sayak Paul sayakpaul Follow Sergio Paniego sergiopaniego Follow Rémi Ouazan Reboul ror Follow Pedro Cuenca pcuenq Follow The matrix multiplication and addition operation 64x64 traces Why does the ProfilerStep#2 take so long? Why is there an offset of ~2.5 ms between the CPU and GPU lanes? The chain of events Why does matmul have an extra CUDA runtime call? Hugging Face Blog is strong enough to treat the story as verified, but the useful part still lies in the context and practical impact. The value of a guide is not just listing steps but helping readers move faster, make fewer mistakes, and know when it is worth applying.
Where to start
Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets --[0--> --]--> Back to Articles Profiling in PyTorch (Part 1): A Beginner's Guide to torch. profiler Published May 29, 2026 Update on GitHub Upvote 31 +25 Aritra Roy Gosthipaty ariG23498 Follow Sayak Paul sayakpaul Follow Sergio Paniego sergiopaniego Follow Rémi Ouazan Reboul ror Follow Pedro Cuenca pcuenq Follow The matrix multiplication and addition operation 64x64 traces Why does the ProfilerStep#2 take so long? Why is there an offset of ~2.
The shortest useful path
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. Hugging Face Blog is strong enough to treat the story as verified, but the useful part still lies in the context and practical impact.
Mistakes to avoid
A common mistake in apps-software stories is jumping straight into the trick while skipping the setup conditions, which makes the move look correct without producing the result people expect. Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets --[0--> --]--> Back to Articles Profiling in PyTorch (Part 1): A Beginner's Guide to torch.
When it makes sense
A guide like this makes sense when the goal is a repeatable, stable result; if the need is unusually specific, readers should still test on a smaller surface first. The value of a guide is not just listing steps but helping readers move faster, make fewer mistakes, and know when it is worth applying. Hugging Face Blog form the main source layer behind the core facts in this piece.
What to keep in mind
The strength of this kind of piece is turning dry information into something readers can use immediately, with 1 source layers keeping the details grounded. Even when the core is settled, the next useful read is still the rollout speed, the real impact, and the switching cost for users or teams. The next thing to watch is rollout speed, regional limits, and whether the update really changes day-to-day habits.