--- title: Why is pytorch compile so fast? | daily.dev description: PyTorch's Inductor compiler uses vertical fusion to combine multiple GPU operations into single Triton kernels, dramatically reducing memory traffic and kernel... canonical: https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Why is pytorch compile so fast? | daily.dev og:description: PyTorch's Inductor compiler uses vertical fusion to combine multiple GPU operations into single Triton kernels, dramatically reducing memory traffic and kernel... og:url: https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov og:image: https://api.daily.dev/og/posts/C9e6SpMOv.png og:image:alt: Why is pytorch compile so fast? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Why is pytorch compile so fast? **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 6 min read · 0 upvotes · 0 comments ## Summary PyTorch's Inductor compiler uses vertical fusion to combine multiple GPU operations into single Triton kernels, dramatically reducing memory traffic and kernel launch overhead. Without fusion, each PyTorch operation (multiply, add, sigmoid) runs as a separate kernel, requiring multiple reads and writes to slow global memory. With fusion via torch.compile, intermediate values stay in fast GPU registers and never touch global memory. A concrete pointwise fusion example shows how eight memory operations collapse to four — a 50% reduction. The post also covers reduction fusion, GEMM+epilogue fusion, prologue fusion, and horizontal fusion, and provides a hands-on example using TORCH_LOGS to inspect generated Triton kernels. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Why Is PyTorch Compile So Fast: Kernel Fusion – PyTorch](https://daily.dev/posts/why-is-pytorch-compile-so-fast-kernel-fusion-pytorch-gq553uop8) · PyTorch · 0 upvotes · 0 comments - [Kernel Fusion in NVIDIA CUDA: Optimizing Memory Traffic and Launch Overhead](https://daily.dev/posts/kernel-fusion-in-nvidia-cuda-optimizing-memory-traffic-and-launch-overhead-vyzarvdyg) · NVIDIA Developer · 0 upvotes · 0 comments - [Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels](https://daily.dev/posts/cutting-llm-memory-by-84-a-deep-dive-into-fused-kernels-qa2yvt9ou) · Towards Data Science · 0 upvotes · 0 comments - [Profiling in PyTorch \(Part 2\): From nn.Linear to a Fused MLP](https://daily.dev/posts/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp-zxpmkydkp) · Hugging Face · 1 upvotes · 0 comments - [From 200 lines to 15: How Helion is rewriting the rules of GPU programming](https://daily.dev/posts/from-200-lines-to-15-how-helion-is-rewriting-the-rules-of-gpu-programming-ewda7l8wq) · Red Hat Developer · 0 upvotes · 0 comments --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#gpu](https://daily.dev/tags/gpu), [#pytorch](https://daily.dev/tags/pytorch) [View this post on daily.dev](https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Why is pytorch compile so fast?","url":"https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov"},"datePublished":"2026-07-24T03:21:05.671Z","dateModified":"2026-07-24T04:02:18.420Z","description":"PyTorch's Inductor compiler uses vertical fusion to combine multiple GPU operations into single Triton kernels, dramatically reducing memory traffic and kernel...","image":"https://media.daily.dev/image/upload/s--ZrL_HSsR--/f_auto/v1722860399/public/Placeholder%2006","thumbnailUrl":"https://media.daily.dev/image/upload/s--ZrL_HSsR--/f_auto/v1722860399/public/Placeholder%2006","isAccessibleForFree":true,"articleSection":"Red Hat Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Red Hat Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/2a8895f3216c4900bdc455585a4ad3e4","url":"https://daily.dev/sources/rhdev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/why-is-pytorch-compile-so-fast--c9e6spmov","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"deep-learning,gpu,pytorch","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Red Hat Developer","item":"https://daily.dev/sources/rhdev"},{"@type":"ListItem","position":3,"name":"Why is pytorch compile so fast?"}]} ```