--- title: Optimizing Data Transfer in Batched AI/ML Inference... description: GPU-to-CPU data transfer can bottleneck batched ML inference workloads just as severely as CPU-to-GPU transfers. Using NVIDIA Nsight Systems profiler on a... canonical: https://daily.dev/posts/optimizing-data-transfer-in-batched-ai-ml-inference-workloads-jvgpnp8x0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Optimizing Data Transfer in Batched AI/ML Inference Workloads | daily.dev og:description: GPU-to-CPU data transfer can bottleneck batched ML inference workloads just as severely as CPU-to-GPU transfers. Using NVIDIA Nsight Systems profiler on a... og:url: https://daily.dev/posts/optimizing-data-transfer-in-batched-ai-ml-inference-workloads-jvgpnp8x0 og:image: https://api.daily.dev/og/posts/jVgPNp8X0.png og:image:alt: Optimizing Data Transfer in Batched AI/ML Inference Workloads 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. # Optimizing Data Transfer in Batched AI/ML Inference Workloads **[Towards Data Science](https://daily.dev/sources/tds)** · 14 min read · 1 upvotes · 0 comments ## Summary GPU-to-CPU data transfer can bottleneck batched ML inference workloads just as severely as CPU-to-GPU transfers. Using NVIDIA Nsight Systems profiler on a DeepLabV3 segmentation model, four optimizations are demonstrated: multi-worker output processing (58% speedup), pre-allocated buffer pools (2x speedup), asynchronous memory copies with pinned memory and CUDA events, and CUDA stream pipelining. Combined, these techniques achieve a 4x throughput improvement by eliminating GPU idle time and parallelizing memory operations with kernel execution. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Optimizing Data Transfer in AI/ML Workloads](https://daily.dev/posts/optimizing-data-transfer-in-ai-ml-workloads-mhsjktwp3) · Towards Data Science · 3 upvotes · 0 comments - [Optimizing Data Transfer in Distributed AI/ML Training Workloads](https://daily.dev/posts/optimizing-data-transfer-in-distributed-ai-ml-training-workloads-c015cc2oy) · Towards Data Science · 0 upvotes · 0 comments - [Unlocking asynchronicity in continuous batching](https://daily.dev/posts/unlocking-asynchronicity-in-continuous-batching-ioe3e7xjh) · Hugging Face · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#nvidia](https://daily.dev/tags/nvidia), [#gpu](https://daily.dev/tags/gpu), [#pytorch](https://daily.dev/tags/pytorch) [View this post on daily.dev](https://daily.dev/posts/optimizing-data-transfer-in-batched-ai-ml-inference-workloads-jvgpnp8x0) ```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":"Optimizing Data Transfer in Batched AI/ML Inference Workloads","url":"https://daily.dev/posts/optimizing-data-transfer-in-batched-ai-ml-inference-workloads-jvgpnp8x0","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/optimizing-data-transfer-in-batched-ai-ml-inference-workloads-jvgpnp8x0"},"datePublished":"2026-01-12T12:05:47.580Z","dateModified":"2026-01-12T12:06:11.642Z","description":"GPU-to-CPU data transfer can bottleneck batched ML inference workloads just as severely as CPU-to-GPU transfers. 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