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OpenAI's custom Jalapeno AI inference ASIC is for OpenAI’s internal use, but company leaves the door open to broader rollout
OpenAI isn’t going up against Nvidia… at least not yet.
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Following the reveal of OpenAI’s Jalapeño ASIC, a clear question formed: Who is this for? That’s not to say the accelerator doesn’t have a purpose, but rather that OpenAI didn’t clearly define what its ambitions were in the hardware space. On one hand, the company suggested it was building ASICs for its own purposes when OpenAI and Broadcom revealed their partnership last year. On the other hand, OpenAI laid out benchmarks comparing Jalapeño to Nvidia’s Blackwell accelerators and doubled down on a multi-generational roadmap at Hot Chips 2026. Jalapeño is built for OpenAI’s compute needs, Richard Ho, Head of Hardware at OpenAI, told Tom’s Hardware Premium. However, the VP says “you could use it for anybody, honestly,” and left the door open for a wider rollout. You can read the full transcript of the interview here.
“We have such a strong demand for compute within the company. It's going to take us a good long time to even fill our own demand, which is growing all the time,” Ho said. “I think that we're going to have our hands full just providing compute for OpenAI for a good long time. That's not to say that it can't be used elsewhere. I believe it could be, but I think our priority is to make sure that OpenAI's compute needs are met first and foremost.”
The competitive positioning of Jalapeño mainly comes down to the benchmarks OpenAI shared during Hot Chips, run on SemiAnalysis’ InferenceX benchmark and comparing Jalapeño to Nvidia’s GB200 and GB300. ASICs are common, but competitive performance for them isn’t common, and for good reason. They’re built to accelerate specific workloads. With Jalapeño, however, OpenAI demonstrated the chip accelerating its own open-weight GPT-OSS model, as well as DeepSeek R1 and Kimi K2.5.
Originally, OpenAI didn’t plan to show benchmarks at Hot Chips, and the company wasn’t sure if it would present at the event at all, Ho told us. The executive reiterated the story OpenAI told on the Hot Chips stage, about how a team of engineers got Kimi and DeepSeek up and running on Jalapeño in the two months between the A0 sample and the Hot Chips presentation.
Although Ho was clear that Jalapeño is being deployed internally and will remain internal for the time being, he certainly left the door open to a wider entry into the hardware market. Speaking on the benchmarks shown at Hot Chips, Ho said: “What we really wanted to demonstrate, to put to rest, the misperception in the industry that our custom inference chip was only for OpenAI models… It’s programmable, and it’s general purpose, and it’s not hard-coded for OpenAI models.”
One possible explanation for reluctance to enter the external hardware market is supply. Ho said “there’s a new baseline for supply,” referring to the past two years of Ho and OpenAI CEO Sam Altman touring fabs and asking for more capacity. Although Ho says “[OpenAI is] in good shape” on the supply front internally, supply to feed external customers is likely a different story.
Jalapeño works for other models, but raw competitive performance wasn’t the main design goal. When I asked about the driving force behind designing Jalapeño, Ho was blunt: “It was efficiency.” The executive pointed to efficiency as a cousin of compute, noting the power-constrained modern AI data center and how a more efficient inference engine represents more effective compute.
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