--- title: The AI Memory Wall: Why LLMs & AI Accelerators Hit a... description: Explains the 'memory wall' concept: over two decades, chip compute has grown roughly 6,500x while memory bandwidth has only grown about 900x, creating a... canonical: https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck | daily.dev og:description: Explains the 'memory wall' concept: over two decades, chip compute has grown roughly 6,500x while memory bandwidth has only grown about 900x, creating a... og:url: https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb og:image: https://api.daily.dev/og/posts/gC384nYkB.png og:image:alt: The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck 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. # The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck **[IBM Technology](https://daily.dev/sources/ibmtechnology)** · 12 min read · 0 upvotes · 0 comments ## Summary Explains the 'memory wall' concept: over two decades, chip compute has grown roughly 6,500x while memory bandwidth has only grown about 900x, creating a widening performance gap. Introduces arithmetic intensity (flops per byte moved) and the roofline model to determine whether a workload is compute-bound or memory-bound, using the NVIDIA B200 GPU (ridge point of 281 flops/byte) as a worked example. Shows that LLM decode is memory-bound (arithmetic intensity ~1) while prefill is compute-bound (intensity >1000), and explains why batching, used in vLLM, SGLang, and TensorRT-LLM, raises arithmetic intensity to push workloads closer to the ridge point and improve throughput. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the ridge point for arithmetic intensity on an NVIDIA B200 GPU? The ridge point for the B200 GPU is about 281 flops per byte, calculated by dividing its peak compute of 2.25 petaflops per second (per single GPU, BF16 dense) by its memory bandwidth of 8 terabytes per second. Workloads with arithmetic intensity below this value are memory bound; above it, they are compute bound. _Engineers sizing GPU workloads can track hardware benchmarks like this through daily.dev._ ### Why is the decode stage of LLM inference memory bound while prefill is compute bound? Decode generates one token at a time with batch size one, giving an arithmetic intensity of roughly one flop per byte, far below a GPU's ridge point, which makes it memory bound. Prefill performs large matrix operations with arithmetic intensity above 1,000 flops per byte, well past the ridge point, making it compute bound instead. _Teams tuning LLM serving pipelines follow explanations like this on daily.dev._ ### How does batching improve LLM inference performance on GPUs? Batching increases arithmetic intensity by reusing the same weights loaded into memory across multiple simultaneous token generation requests, performing more math operations per byte moved. This shifts the workload closer to the GPU's ridge point, reducing the memory bottleneck, which is why serving engines like vLLM, SGLang, and TensorRT-LLM rely on continuous batching. _Developers choosing between LLM serving frameworks compare batching strategies on daily.dev._ ## Similar posts on daily.dev - [Your GPU Is a Kitchen With One Tiny Door](https://daily.dev/posts/your-gpu-is-a-kitchen-with-one-tiny-door-rbbxjvpyf) · Medium · 1 upvotes · 0 comments - [How a GPU Actually Works](https://daily.dev/posts/how-a-gpu-actually-works-t655zbnjv) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [The Real Challenge Limiting AI Models Today](https://daily.dev/posts/the-real-challenge-limiting-ai-models-today-u5uh7pwbn) · Towards Data Science · 1 upvotes · 0 comments - [\[2601.05047\] Challenges and Research Directions for Large Language Model Inference Hardware](https://daily.dev/posts/2601-05047-challenges-and-research-directions-for-large-language-model-inference-hardware-t0lnvakom) · Hacker News · 1 upvotes · 0 comments - [How I doubled my GPU efficiency without buying a single new card](https://daily.dev/posts/how-i-doubled-my-gpu-efficiency-without-buying-a-single-new-card-e57gspube) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb) ```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":"The AI Memory Wall: Why LLMs \u0026 AI Accelerators Hit a Bottleneck","url":"https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb"},"datePublished":"2026-10-18T11:09:21.857Z","dateModified":"2026-10-18T11:09:41.739Z","description":"Explains the 'memory wall' concept: over two decades, chip compute has grown roughly 6,500x while memory bandwidth has only grown about 900x, creating a...","image":"https://i.ytimg.com/vi/4b6KpFN7Rio/sddefault.jpg","thumbnailUrl":"https://i.ytimg.com/vi/4b6KpFN7Rio/sddefault.jpg","isAccessibleForFree":true,"articleSection":"IBM Technology","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":"IBM Technology","logo":"https://media.daily.dev/image/upload/s--OXHaOiWX--/f_auto/v1715498011/logos/ibmtechnology","url":"https://daily.dev/sources/ibmtechnology"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-inference,vllm","timeRequired":"PT12M","video":{"@type":"VideoObject","name":"The AI Memory Wall: Why LLMs \u0026 AI Accelerators Hit a Bottleneck","description":"Explains the 'memory wall' concept: over two decades, chip compute has grown roughly 6,500x while memory bandwidth has only grown about 900x, creating a...","thumbnailUrl":"https://i.ytimg.com/vi/4b6KpFN7Rio/sddefault.jpg","uploadDate":"2026-10-18T11:09:21.857Z","duration":"PT12M","url":"https://api.daily.dev/r/gC384nYkB","embedUrl":"https://www.youtube.com/embed/4b6KpFN7Rio"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"IBM Technology","item":"https://daily.dev/sources/ibmtechnology"},{"@type":"ListItem","position":3,"name":"The AI Memory Wall: Why LLMs \u0026 AI Accelerators Hit a Bottleneck"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/the-ai-memory-wall-why-llms-ai-accelerators-hit-a-bottleneck-gc384nykb#faq","mainEntity":[{"@type":"Question","name":"What is the ridge point for arithmetic intensity on an NVIDIA B200 GPU?","acceptedAnswer":{"@type":"Answer","text":"The ridge point for the B200 GPU is about 281 flops per byte, calculated by dividing its peak compute of 2.25 petaflops per second (per single GPU, BF16 dense) by its memory bandwidth of 8 terabytes per second. 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