Citrini analyst Jukan said OpenAI’s in-house AI chip Jalapeño may struggle to scale to the level of Nvidia’s Rubin platform if its HBM4 high-bandwidth memory supply is tied entirely to Samsung. In his view, that dependence could put a ceiling on production ramp-up. He added that if OpenAI wants to deploy Jalapeño at a larger scale, it may need to relax some HBM4 requirements, including transfer-speed specifications, so it can use HBM4 products from additional suppliers and ease supply-chain constraints. Earlier on the same day, BlockBeats reported that OpenAI had released new test results for Jalapeño, describing it as an AI inference chip that beat Nvidia’s GB200 and GB300 superchips in the InferenceX benchmark. OpenAI said the chip delivered 1.5x to 1.9x the AI work per watt of competing products across models including GPT-OSS 120B, DeepSeek R1, and Kimi K2.5 1T, while cutting end-to-end latency by 1.7x to 3.6x.
BlockBeats reported on Aug. 26 that Citrini analyst Jukan said OpenAI’s in-house AI chip, Jalapeño, may not be able to scale to the level of Nvidia’s Rubin platform.
Jukan said a key constraint could come from memory supply. If OpenAI relies entirely on Samsung for HBM4 high-bandwidth memory, the chip’s production ramp could face an upper limit.
He also said that if OpenAI wants to expand Jalapeño deployment further, it may need to loosen some HBM4 specifications, including transfer-speed requirements, so it can adopt HBM4 products from other suppliers and reduce supply-chain pressure.
Earlier in the day, BlockBeats reported that OpenAI had released the latest test results for its self-developed AI inference chip Jalapeño. According to that report, the chip outperformed Nvidia’s GB200 and GB300 superchips in the InferenceX benchmark. On models including GPT-OSS 120B, DeepSeek R1, and Kimi K2.5 1T, OpenAI said Jalapeño delivered 1.5x to 1.9x the AI work per watt of competing products and reduced end-to-end latency by 1.7x to 3.6x.
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