OpenAI’s first reported custom semiconductor, called Jalapeno, is an inference chip intended to help the company serve AI models more efficiently. OpenAI says it is developing accelerators and data-center systems with Broadcom, but the partnership’s announced 10-gigawatt target is for systems—not a published capacity rating for Jalapeno. The chip’s deployment remains a goal, not a confirmed live service.
What is OpenAI’s Jalapeno chip?
In an interview published by Forbes Australia on October 13, 2026, OpenAI vice president of hardware Richard Ho described Jalapeno as the company’s first custom semiconductor and an inference chip. Inference is the computing work involved in running a trained model to generate responses or other outputs. Ho said the chip is programmable and flexible, designed to be somewhat more specialized than a general-purpose GPU while focusing on transformers and large language models.
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Ho said the goal is to retain adaptability as models change, rather than build a device that works only with OpenAI models. That is OpenAI’s description of its design intent; the interview did not provide independent performance tests, benchmark results, or a detailed technical specification.
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Why is OpenAI designing its own hardware?
Ho said the effort grew from early discussions about whether OpenAI could get the computing hardware it needed from general-market suppliers such as AMD, Nvidia, and Intel. Rather than rely only on off-the-shelf accelerators, the company chose to pursue a device designed around its own workloads and systems.
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The strategic aim, as Ho described it, is to coordinate more of the infrastructure stack—from data centers and chips to systems, software, and models—so the components can be optimized together. OpenAI’s October 13, 2025 announcement similarly said its hardware work would incorporate learning from its models and products. Ho identified “intelligence per watt” as the key efficiency measure: how much useful model capability can be delivered for the energy consumed.
That rationale is not a published savings result. Ho said he had not analyzed how much the chip might save, and Forbes reported that an OpenAI spokesperson said the company had no estimate. The available reporting does not establish a quantified reduction in cost, power use, or operating expense.
What are OpenAI and Broadcom each doing?
OpenAI says it is designing AI accelerators and systems in collaboration with Broadcom. Broadcom is the accelerator-development partner and is providing Ethernet and other connectivity solutions for the systems. Ho said OpenAI chose Broadcom after evaluating potential partners; he pointed to relevant analog IP and prior working relationships among members of his team, including experience with Broadcom on Google’s first TPU.
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This description does not establish that Broadcom alone manufactures Jalapeno. The Forbes interview and OpenAI announcement do not identify the chip’s current foundry or confirm its manufacturing status. The partner roles that are public are design collaboration and networking—not a complete account of fabrication or the supply chain.
What does the 10-gigawatt figure mean?
OpenAI’s 2025 announcement described 10 gigawatts of custom accelerator systems under the Broadcom collaboration. It said deployment was targeted to begin in the second half of 2026 and finish by the end of 2029. Those are the company’s announced system-level targets. They do not tell readers how much capacity Jalapeno itself has, and they do not confirm that the systems have been deployed.
In the 2026 interview, Ho said OpenAI had not publicly shared Jalapeno’s gigawatt capacity. The 10-gigawatt figure should therefore not be described as the capacity of the chip, or as a measure of how many individual Jalapeno devices will be installed.
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When might Jalapeno serve users?
Ho said OpenAI’s intent was to have something in production in the first half of 2027, with some users potentially running some models on Jalapeno by then. He explicitly called that a stretch goal and said software and other preparation work remained. It is a tentative target, not confirmation of current user access or a firm full-production date.
OpenAI’s 2025 rollout schedule and Ho’s later goal describe different milestones: the former covers deployment of the broader custom accelerator systems, while the latter is his stated aspiration for early production use. Neither statement, on its own, demonstrates that Jalapeno is already serving users.
How does custom hardware compare with purchased accelerators?
The reporting explains OpenAI’s reasons for adding a custom option, but it does not establish that Jalapeno is superior to general-purpose accelerators. The distinction is between a design tailored more closely to OpenAI’s workloads and commercially available hardware that can serve a broader range of customers and uses.
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| Factor | General-purpose accelerators | OpenAI’s custom inference hardware |
|---|---|---|
| Workload focus | General-market products from suppliers including AMD, Nvidia, and Intel; exact capabilities vary by product. | Ho describes Jalapeno as more specialized than a general GPU and oriented toward transformers and large language models. |
| Adaptability | No product-specific comparison is established in the interview. | Ho says the design is programmable and intended to adapt as models change, not to work exclusively with OpenAI models. |
| Efficiency and performance | No head-to-head results are provided. | OpenAI’s stated objective is improved intelligence per watt; independent measurements and quantified savings are not provided. |
| System integration | The reporting does not compare vendors’ integration approaches. | OpenAI says it is designing accelerators and systems with Broadcom and aims to optimize across hardware, software, models, and data centers. |
| Availability and maturity | These are established supplier categories, but the article does not compare specific products or supply levels. | Ho’s first-half 2027 production goal is a stretch goal; consumer or outside-customer access is not established. |
Will Jalapeno replace Nvidia or be available to the public?
Ho characterized Jalapeno as an addition to OpenAI’s compute fleet, which he said would continue to include Nvidia, AMD, Cerebras, and other suppliers. He described the custom chip as a way to supplement a system facing strong demand for more compute, not as an announced replacement for Nvidia or the company’s other suppliers.
OpenAI’s 2025 announcement said deployments would cover its facilities and partner data centers. Ho said external sale or cloud access was not the current focus while OpenAI works to meet internal demand. The reporting does not establish a consumer purchase option, repair or replacement parts, or a public cloud service for Jalapeno.
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What remains unknown about the chip?
The reporting leaves important implementation and commercial details open. It does not establish a confirmed full-production date, Jalapeno’s individual capacity, a price, deal financial terms, independently measured performance, or a quantified cost saving. Nor does it confirm a foundry or current fabrication status.
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Ho said roughly 100 people were involved in the overall effort, with an average near 70 during development. He said that figure covered more than chip design: it included systems work such as rack and board design, power, thermal considerations, supply chain, and software. Forbes also reported OpenAI’s claim that it built the chip in nine months; that duration is a company-reported figure, not an independently audited development timeline.
An earlier Reuters report from February 2025 described a proposed training-capable chip and a then-current expectation of TSMC mass production in 2026. That was an earlier reported plan. The October 2026 Forbes interview identifies Jalapeno as an inference chip and does not confirm the earlier report’s training capability, manufacturing partner, or production expectation as facts about Jalapeno.
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