OpenAI Publishes First Benchmarks for Its Jalapeño Inference Chip Against Nvidia Systems

OpenAI released the first performance results for Jalapeño, the inference-only accelerator it developed with Broadcom. Across three models, the company reports 1.5 to 1.9 times higher peak mixed tokens per second per kilowatt and 1.7 to 3.6 times lower end-to-end latency than the Nvidia GB200 and GB300 systems it selected for comparison. On Kimi K2.5, the largest model in the set, it claims roughly 1.5 times the performance per watt and 3.4 times lower latency.

The power figures are the substance of the claim. Jalapeño is a 700-watt package against 1,400 watts for the GB300, and Tom's Hardware reports that measured draw stayed at or below 550 watts during testing. In a buildout where electricity and rack density set the upper limit on deployable capacity, roughly half the package power for comparable throughput changes how much serving capacity fits behind a given substation.

The caveats are real and OpenAI states most of them. Jalapeño cannot train. The comparison uses SemiAnalysis's public InferenceX suite but the runs were executed and reported by OpenAI rather than by an independent lab. The target is also Nvidia's Blackwell-generation systems, not Vera Rubin, which has only just begun shipping. An application-specific integrated circuit built for one workload beating a general-purpose graphics processing unit on that workload is the expected result, not a surprise.

What matters commercially is not whether Jalapeño beats Nvidia in the abstract. It is that OpenAI, the largest single buyer of inference capacity, now has a credible in-house alternative for serving its own traffic and a published number to cite in every future procurement conversation.