via TechCrunch AI
OpenAI Unveils Its First Custom Chip, Built with Broadcom
On Wednesday, OpenAI unveiled its first custom-built inference processor, designed and manufactured in collaboration with Broadcom. Named Jalapeño, the new chip is purpose-built for the unique demands of OpenAI’s inference systems. The company noted that its own AI models assisted in the chip’s development.
While still in testing, OpenAI reports that early results show significantly better performance-per-watt compared to current state-of-the-art alternatives.
The partnership was officially announced in October, but OpenAI’s chip ambitions had long been rumored as a strategy to reduce reliance on Nvidia’s GPUs. Google and Amazon have similarly developed custom AI accelerators—silicon tailored to speed up machine learning workloads.
OpenAI president Greg Brockman discussed the company’s approach to chip development on its in-house podcast shortly after the Broadcom partnership was revealed. “We have a deep understanding of the workload,” he said. “We’ve really been looking for specific workloads that are underserved, and asking how we can build something that will be able to accelerate what’s possible?”
Jalapeño is specifically designed for inference—the process of running pre-trained AI models in response to user commands. In its announcement, OpenAI emphasized the chip’s low operating cost when running real-time coding models. More performance-intensive tasks like pre-training will likely still rely on Nvidia hardware, but even modest reductions in inference costs could significantly improve OpenAI’s bottom line.
Optimizing inference systems may prove crucial to the economics of AI in 2026 and beyond—and it’s likely to occur at every level of the stack. OpenAI is already building agentic products like Codex and the models that power them, along with data centers to run those models. Moving into purpose-built chips allows the company to extend this control even further.
As OpenAI wrote in its announcement: “OpenAI is not only developing frontier models or building products on top of them; it is designing the infrastructure underneath them: chip architecture, kernels, memory systems, networking, scheduling, deployment systems, and product experience. Because OpenAI operates across the stack, each layer can be optimized around the same goal: making its models faster, more reliable, and more affordable for users.”