Anthropic claims it has broken free of its Nvidia dependence with a multi-chip strategy. The company says it ported its own AI model, Claude, to AMD GPUs in just three days, with almost no human intervention.
Nvidia's biggest competitive edge is the combination of hardware and software, specifically CUDA, the software ecosystem that locks developers into Nvidia chips. Porting AI models to other chip architectures takes so much effort that many companies won't even try. Anthropic now claims in an investor presentation that it has solved this problem using AI itself.
According to Tom Brown, co-founder and Chief Compute Officer at Anthropic, the company now trains and runs Claude on all four major chip families. Anthropic made an "unconventional bet" on a multi-chip strategy early on, at a time when most of the industry considered the effort not worth it. Each chip needs its own custom software and inference stack, which is expensive and time-consuming.
But Anthropic has been investing in this work for years and has since become "incredibly good" at it, Brown says.
The company's most concrete test came with AMD GPUs. Until now, bringing up a new chip platform kept dozens of engineers busy for months. This year, Anthropic used AMD GPUs for the first time and let its own frontier model, Mythos, do the work. According to Anthropic, Claude was running on AMD after just three days, with almost no human intervention. The model ported or wrote nearly 100 percent of the kernels itself.
If these figures are accurate, the software overhead, long considered Nvidia's strongest competitive advantage, would shrink dramatically because of AI. Companies could switch more easily between chip providers and pick the most affordable or readily available option.
In a compute market where supply is tight, a multi-chip approach lets companies focus on the best price-performance ratio, regardless of the vendor. Some chips are better for training, while others are better for inference. And because of its dominance in the AI chip market, Nvidia currently enjoys a margin of more than 70 percent, which is unusually high for hardware.
Anthropic treats compute from different vendors, chip architectures, and locations as one interchangeable pool. The company says it gets more out of every dollar of compute than any other frontier AI lab.
Beyond its multi-chip strategy, Anthropic relies on what it calls compute multipliers, research breakthroughs that squeeze more performance from the same physical computing capacity. According to Anthropic, these multipliers build on one another so that each new breakthrough amplifies the effect of the previous one.
Eventually, Claude should be able to discover such compute multipliers on its own. That would amount to recursive self-improvement, a concept that's intensely debated in AI research but hasn't been proven yet.
Anthropic is also building its own data center capacity through the "Anthropic Direct" program. Instead of buying computing power only as a cloud service, the company leases chips and operates them in purpose-built data centers. The goal is to reduce total cost of ownership.
The figures come from Anthropic's investor presentation and haven't been independently verified. But they show that at least one major AI lab no longer considers Nvidia's CUDA dominance insurmountable and that AI models themselves could become a tool for breaking hardware dependencies.
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