Anthropic states it can now switch from Nvidia to AMD chips in three days, a task that previously took six to twelve months.
The company says it has overcome its reliance on Nvidia through a multi-chip strategy. Co-founder Tom Brown told investors the firm trains and runs Claude on all four major chip families, treating them as one interchangeable compute pool.
Nvidia’s main advantage combines hardware with CUDA, the software ecosystem that locks developers into its chips. Moving AI models to other architectures usually requires so much effort that many firms do not attempt it. Anthropic claims to have solved this by using AI to do the work.
Porting Claude to AMD
Anthropic’s most concrete test involved AMD GPUs. Until this year, bringing up a new platform kept dozens of engineers busy for months. The firm used AMD GPUs for the first time and let its own frontier model, Mythos, perform the porting.
Claude was running on AMD after just three days with almost no human intervention. The model wrote or ported 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. Nvidia currently enjoys a margin of more than 70 percent due to its dominance in the AI chip market.
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.
Compute multipliers aim to widen the lead
Beyond its multi-chip strategy, Anthropic relies on what it calls compute multipliers. These are 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 is intensely debated in AI research but has not 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 have not 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.
Both major GPU makers now have deep financial ties to Anthropic. In November 2025, as part of a $45 billion deal with Microsoft, Nvidia committed to investing up to $10 billion in Anthropic. According to Anthropic’s S-1/A registration statement filed with the SEC on November 10, 2026, Nvidia is also participating in a conditional private placement totaling $26 billion, in which seven technology companies are subscribing to shares at the IPO price.
AMD announced a strategic investment of up to $5 billion in July 2026. In return, Anthropic plans to deploy up to two gigawatts of AMD Instinct MI455X GPUs in Helios rack-scale systems, with the first gigawatt phase beginning in the first half of 2027.
What it means
For people making things, the ability to port models in days rather than months means less time spent waiting for specific hardware to become available. Teams can switch to cheaper or faster options without needing a massive engineering overhaul. The main change is a shift in how long it takes to adapt software to new silicon, not a change to the underlying model training or customer privacy policies.
