--- title: How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip description: OpenAI detailed how it used its own large language models, including precursors to GPT-6 Astra, to accelerate the design of its Jalapeño AI accelerator chip,... canonical: https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip | daily.dev og:description: OpenAI detailed how it used its own large language models, including precursors to GPT-6 Astra, to accelerate the design of its Jalapeño AI accelerator chip,... og:url: https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0 og:image: https://api.daily.dev/og/posts/kipHVnkD0.png og:image:alt: How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip 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. # How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip **[IEEE Spectrum](https://daily.dev/sources/ieeespectrum)** · 9 min read · 1 upvotes · 0 comments ## Summary OpenAI detailed how it used its own large language models, including precursors to GPT-6 Astra, to accelerate the design of its Jalapeño AI accelerator chip, taking it from architecture concept to first silicon in under 20 months and from RTL to tapeout in nine months. The workflow leveraged Google's open-source XLS high-level synthesis toolchain, letting AI models work on software-like design tasks such as writing DSLX and C++ code that XLS converts to Verilog. AI-guided software optimization boosted a DeepSeek attention kernel benchmark from 0.31% to 88.94% of theoretical peak performance in about 40 hours, and AI-guided physical design cut matrix multiplication unit area by 10% versus a human baseline. Broadcom handled backend physical design and manufacturing, while outside experts from Verkor.io and academia offered mixed views on how much credit belongs to AI versus Broadcom's involvement, and predicted backend design automation will improve rapidly with newer models. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How did OpenAI use large language models to speed up designing its Jalapeño AI chip? OpenAI built its front-end chip design workflow around Google's open-source XLS high-level synthesis toolchain, letting engineers write DSLX and C++ code that XLS converts to Verilog, since LLMs perform better on software-like tasks than raw hardware description languages. This helped take Jalapeño from architecture concept to first silicon in under 20 months, with only nine months from first RTL to tapeout. _Track how AI-assisted hardware design workflows evolve by following chip design coverage on daily.dev._ ### What performance does OpenAI's Jalapeño chip offer compared to Nvidia's GB300? Jalapeño delivers up to 13.4 petaflops of 4-bit compute, 232 gigabytes of memory, and 15.4 terabytes per second of memory bandwidth. OpenAI-cited benchmarks show it cuts end-to-end latency by up to 3.6x versus Nvidia's GB300, the chip OpenAI currently relies on, while consuming less power, though real-world gains in production remain unverified. _Compare emerging AI accelerator benchmarks like this one on daily.dev before betting on new inference hardware._ ### How much did AI improve chip benchmark performance after Jalapeño's first silicon came back from the foundry? After first chips arrived in May, OpenAI pointed internal AI models at writing benchmark software, raising performance on DeepSeek's multi-head latent attention kernel benchmark from 0.31 percent to 88.94 percent of theoretical peak in roughly 40 hours. OpenAI's hardware VP Richard Ho says this result is repeatable, shortening the gap between silicon delivery and production ramp-up. _Follow rapid AI-driven optimization results like this on daily.dev to gauge what's achievable in your own stack._ --- Tags: [#llm](https://daily.dev/tags/llm), [#hardware](https://daily.dev/tags/hardware), [#openai](https://daily.dev/tags/openai) [View this post on daily.dev](https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0) ```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":"How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip","url":"https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0"},"datePublished":"2026-09-14T14:55:41.681Z","dateModified":"2026-09-14T14:56:08.350Z","description":"OpenAI detailed how it used its own large language models, including precursors to GPT-6 Astra, to accelerate the design of its Jalapeño AI accelerator chip,...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7764dfc3cdf16c2df5c308c26a0d364e?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/7764dfc3cdf16c2df5c308c26a0d364e?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"IEEE Spectrum","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":"IEEE Spectrum","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/b123f5e8420e492ab36f6adb30c1793a","url":"https://daily.dev/sources/ieeespectrum"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,hardware,openai","timeRequired":"PT9M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"IEEE Spectrum","item":"https://daily.dev/sources/ieeespectrum"},{"@type":"ListItem","position":3,"name":"How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/how-openai-used-its-own-llms-to-design-its-jalape-o-chip-kiphvnkd0#faq","mainEntity":[{"@type":"Question","name":"How did OpenAI use large language models to speed up designing its Jalapeño AI chip?","acceptedAnswer":{"@type":"Answer","text":"OpenAI built its front-end chip design workflow around Google's open-source XLS high-level synthesis toolchain, letting engineers write DSLX and C++ code that XLS converts to Verilog, since LLMs perform better on software-like tasks than raw hardware description languages. This helped take Jalapeño from architecture concept to first silicon in under 20 months, with only nine months from first RTL to tapeout. Track how AI-assisted hardware design workflows evolve by following chip design coverage on daily.dev."}},{"@type":"Question","name":"What performance does OpenAI's Jalapeño chip offer compared to Nvidia's GB300?","acceptedAnswer":{"@type":"Answer","text":"Jalapeño delivers up to 13.4 petaflops of 4-bit compute, 232 gigabytes of memory, and 15.4 terabytes per second of memory bandwidth. OpenAI-cited benchmarks show it cuts end-to-end latency by up to 3.6x versus Nvidia's GB300, the chip OpenAI currently relies on, while consuming less power, though real-world gains in production remain unverified. Compare emerging AI accelerator benchmarks like this one on daily.dev before betting on new inference hardware."}},{"@type":"Question","name":"How much did AI improve chip benchmark performance after Jalapeño's first silicon came back from the foundry?","acceptedAnswer":{"@type":"Answer","text":"After first chips arrived in May, OpenAI pointed internal AI models at writing benchmark software, raising performance on DeepSeek's multi-head latent attention kernel benchmark from 0.31 percent to 88.94 percent of theoretical peak in roughly 40 hours. OpenAI's hardware VP Richard Ho says this result is repeatable, shortening the gap between silicon delivery and production ramp-up. Follow rapid AI-driven optimization results like this on daily.dev to gauge what's achievable in your own stack."}}]} ```