Jalapeño’s first results show industry-leading speed and efficiency in AI inference
What's happening
OpenAI published first results for its Jalapeño inference chip, reporting industry-leading speed and efficiency. Tested on SemiAnalysis' InferenceX benchmark across models including GPT-OSS 120B, DeepSeek R1 670B and Kimi K2.5 1T, Jalapeño delivered 1.5x to 1.9x more throughput per kilowatt and 1.7x to 3.6x lower end-to-end latency versus Nvidia GB200 and GB300 rack systems. Multiple outlets characterize Jalapeño as outperforming Nvidia Blackwell (GB300/GB200) in those benchmarks, and OpenAI says the chip was built for fast inference at scale and shows more tokens per user and more throughput per kilowatt than the current state of the art.
Why it's trending
Because OpenAI released concrete InferenceX benchmark numbers that claim clear wins over Nvidia GB200/GB300, sparking immediate comparison and debate.
Signal9.0× its usual volume, confirmed across 3 independent source types.
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If Jalapeño really delivers 1.5x to 1.9x throughput per kW and up to 3.6x lower latency than Nvidia GB300, the GPU hegemony story just got complicated, and cloud bills should be nervous now.
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Original sources18
- New OpenAI chip promises faster, cheaper AI than Nvidia’sLos Angeles TimesAug 26
- OpenAI Just Dropped Benchmarks for Their Own Chip, Jalapeño, and It's Beating Nvidia's GB300
After months of vague teaser talk, OpenAI put real numbers on the table for Jalapeño, the inference chip built with Broadcom. Testing ran through InferenceX, SemiAnalysis's public benchmark, across three open models: GPT-OSS 120B, DeepSeek R1 670B, and Kimi K2.5 1T. Against Nvidia's GB200 and GB300 rack systems: 1.5x to 1.9x more throughput per kilowatt 1.7x to 3.6x lower end-to-end latency At the
r/OpenAIAug 25 - OpenAI claims its new chips can outperform Nvidia processors in testsHackerNewsAug 25
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