OpenAI publishes the first measured results for its Jalapeño inference chip
OpenAI published the first measured results for Jalapeño, the custom inference accelerator it announced in June. On the public InferenceX benchmark with GPT-OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T, OpenAI reports 1.5–1.9× more work per watt at peak throughput and 1.7–3.6× lower end-to-end latency than the named comparison systems, with deployment inside OpenAI planned by year-end.
Why it matters: Working first-party silicon and public operating-point data make this more than a roadmap claim. If the results reproduce at scale, custom inference hardware could change latency, power, capacity, and model-serving economics while reducing—but not…