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If hyperscalers scale custom ASICs for inference, Nvidia could face margin pressure on high-volume inference workloads and be pressured to defend its GPU-centric ecosystem; near-term deployment signals potential incremental risk to Nvidia's inference business, though training dominance and CUDA ecosystem remain long-term advantages.
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OpenAI's Jalapeño, built with Broadcom, targets inference efficiency and faster responses, signaling rising pressure on Nvidia's dominance in AI inference. Hyperscalers Google, AWS, and Meta are pursuing their own silicon, potentially compressing Nvidia's inference margins even as Nvidia remains essential for training and its CUDA ecosystem. Industry forecasts suggest custom ASICs may surpass GPU volumes by 2028, implying longer-term capex and margin shifts for Nvidia.
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