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    Home»Big Tech & AI»China Falling Behind AI Arms Race After Missing Access to US Blackwell GPUs, Says Atreides CIO Gavin Baker

    China Falling Behind AI Arms Race After Missing Access to US Blackwell GPUs, Says Atreides CIO Gavin Baker

    By Henry KanapiNovember 20, 20252 Mins Read
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    Atreides Management chief investment officer Gavin Baker says the United States is pulling ahead in the global artificial intelligence hardware race, fueled by the rollout of Nvidia’s Blackwell architecture.

    In a new commentary on X, Baker says China’s semiconductor ecosystem is losing critical ground as US firms ramp the next generation of compute.

    “Blackwell will likely significantly increase the gap between the American frontier models and Chinese open source models.”

    He says domestic Chinese AI chips are no longer close enough to compete on performance. He says the gap is widening faster than analysts appreciate.

    “The domestic Chinese semiconductors are much further behind Blackwell relative to their performance vs. Hopper a year ago. This will only increase the barriers to entry for frontier models as new entrants will have to rely on increasingly outdated Chinese open source models as they attempt to catch up.”

    Baker says China is now on the back foot after missing a window of access to US hardware, noting that the strategic gap will grow even more as US defense spending fuels domestic resource and manufacturing capacity.

    “I think China will really regret not leaning into Trump’s willingness to sell them the ‘B30.’ Blackwell will alter the relative leverage of the US vs. China, especially as the truly immense DoD effort to increase domestic rare earth mining and refining pays off over the next two years. I think the technological solutions to refining that are being pursued are underappreciated and at least some are likely to succeed.”

    The Nvidia Blackwell B200 GPU is a significant leap from the Hopper-architecture H100 GPU. For AI inference, it delivers up to 30 times faster performance than the H100 system, specifically for trillion-parameter large language models (LLMs), and is up to four times faster than the H100 for LLM training. It also offers up to 25 times better energy efficiency for inference workloads and comes with 8 TB/s of memory bandwidth, which is approximately 2.4 times faster than the H100’s 3.35 TB/s.

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    Blackwell China Gavin Baker Nvidia US
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