OpenAI の「Jalapeño」チップの数学的背景

OpenAI’s financial trajectory hinges heavily on infrastructure costs, a reality that drove the development of the new custom OpenAI Jalapeño chip. Developed in collaboration with Broadcom, the application-specific integrated circuit (ASIC) represents a direct attempt to mitigate the heavy capital expenditure associated with third-party hardware.

While Nvidia currently commands an estimated 75% profit margin on its high-end processors, OpenAI operates on tighter margins, keeping roughly 33 cents of profit on each dollar generated after accounting for its massive operational expenses. The financial burden of running large language models at scale is severe.

Last year, keeping ChatGPT servers responsive had cost OpenAI a staggering US$8.4 billion. With the platform now attracting 900 million weekly users, that operational cost is projected to reach approximately US$14 billion this year. Over the next eight years, OpenAI has committed roughly US$1.4 trillion to computing power, a massive bet for a company currently generating US$25 billion in annual revenue.

Designing Hardware for LLM Inference

The OpenAI Jalapeño chip, dubbed as the company’s first “Intelligence Processor”, is built specifically for large language model (LLM) inference rather than general-purpose AI workloads. OpenAI provided the core architectural design based on its specific model roadmaps and serving systems, while Broadcom managed the silicon engineering and high-performance networking integration.

TSMC handles the physical manufacturing in Taiwan, and Celestica is tasked with building the board and rack systems. According to OpenAI, early lab samples are already running frontier workloads, including an unreleased GPT-5.3-Codex-Spark model, at target production frequency and power.

Richard Ho, head of OpenAI’s hardware program, noted that the architecture minimizes data movement to push realized utilization closer to its theoretical peak performance. Unlike general-purpose accelerators adapted from legacy AI workloads, this architecture specifically balances compute, memory, and networking resources to solve the data-movement bottlenecks native to interactive LLM serving.

To achieve this at scale, the platform integrates Broadcom’s Tomahawk networking silicon directly into the design, allowing the custom processors to communicate across massive, clustered data center environments.

The vertical integration flywheel

By moving into custom silicon, OpenAI shifts from being a mere software layer to a vertically integrated infrastructure company. This full-stack strategy spans the entire pipeline: chip architecture, software kernels, memory systems, network scheduling, and the final application layer. Much like Apple’s tight coupling of proprietary hardware and iOS, OpenAI can now optimize its infrastructure around its exact internal model roadmaps.

This integration feeds a continuous operational flywheel. Enhanced infrastructure efficiency lowers the cost of both training and serving models. More affordable serving leads to better, more responsive products, which drives user volume and revenue to be reinvested back into the next generation of custom infrastructure.

Overcoming the late-mover advantage

By introducing its own silicon, OpenAI enters a landscape where its primary competitors have spent nearly a decade developing proprietary hardware. Google began deploying its Tensor Processing Units (TPUs) in 2015 and now controls roughly a quarter of global AI computing capacity outside of Nvidia’s supply chain.

Amazon has shipped over one million of its custom chips, while Meta and Microsoft continue to scale their own infrastructure.

“Jalapeño is part of our long-term full-stack infrastructure strategy to make compute more abundant,” said Greg Brockman, president and co-founder of OpenAI. “By designing more of the stack ourselves, we can serve more intelligence with greater efficiency.”

To close this timeline gap, OpenAI accelerated the development phase. The OpenAI Jalapeño chip transitioned from a blank-slate design to manufacturing tape-out—the final step before physical production—in just nine months. The engineering teams achieved this timeline by utilizing OpenAI’s own language models to automate and optimize portions of the hardware design process.

This creates a unique feedback loop where the models served to users are actively being leveraged to build the physical infrastructure that will run future iterations. Initial deployment of the hardware into data centres is scheduled to begin by the end of 2026.

Broadcom CEO Hock Tan confirmed that the rollout will scale alongside infrastructure partners, including Microsoft, to prepare for gigawatt-scale data centre integration.

(Photo by OpenAI)

See also: Omio scales travel product development using OpenAI models

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