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Anthropic revealed that its model learned to transplant and run on a new GPU in just 3 days, causing the outside world to question whether Huida’s CUDA moat is loose, but there are still advantages in its ecosystem and developer base.
except CEOJen-Hsun Huang In addition to being embroiled in scandal, Nvidia’s advantage in the established AI computing market through the CUDA platform is rapidly diminishing.anthropic A slide at its IPO investment briefing noted that its Cloud Mythos Preview model took just three days to figure out how to run other models on a new GPU, which is significantly less than the 6 to 12 months it typically takes to create inferential operations for a new computing platform.
Wccftech pointed out that this slide shows that Anthropic now treats the GPU as an interchangeable computing unit, and the barriers to switching between platforms have been significantly reduced. This has led some to claim that Huida’s CUDA moat may no longer exist.
However, despite this, it is still too early to declare Huida’s CUDA platform dead, especially because it still includes a huge ecosystem of libraries and tools and has been continuously optimized for many years, not to mention that a large number of developers continue to develop based on it.
Huida has done its best to ensure the stability of the CUDA moat over the past few years, including providing free GPUs and training for university researchers, integrating the Huida Collective Communication Library (NCCL) with proprietary hardware such as NVLink and InfiniBand.
Summary FAQ
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Anthropic reported at the IPO investment briefing that Cloud Mythos Preview took only 3 days to figure out how to run other models on the new GPU, which is much faster than the long-term migration process that was common in the past.
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Because this shows that the threshold for switching between GPU platforms is being lowered, the market is beginning to believe that the technical advantage that Huida relies on CUDA to establish may no longer be as unbreakable as it was before.
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Because CUDA still has a huge database, tools, and years of optimization accumulation, and a large number of developers continue to use it; Huida also strengthens the ecosystem through free GPUs, training, and exclusive hardware integrations.
