Why go slow when you can go Ultrafast? Even @udayruddarraju is making the point pretty clearly today. AMD is showing off OpenAI visiting its labs. Cerebras already proved it can deliver fast inference. Apparently, just not fast enough to win once again. Meanwhile, GPT-6 Astra Ultrafast is running on NVIDIA Blackwell TODAY. And I think the more interesting story isn’t just the hardware. It’s what OpenAI is doing with it. Philippe Tillet - OpenAI’s inference lead, says Astra can use NVIDIA’s tooling and documentation to generate high-performance kernels optimized for Blackwell and Rubin. @udayruddarraju goes one step further: OpenAI is using its own models to optimize inference running on NVIDIA GPUs. Better kernels, better scheduling, better inference software and model-driven optimization can keep extracting more from the same installed base. That’s a pretty important point for the useful life and economics of Blackwell. And this is also why I don’t think inference leadership changes overnight. Choosing another accelerator isn’t simply: “Vendor B is cheaper/faster. Let’s switch.” Models and production inference stacks have to be qualified and optimized for a new architecture. Software matters. Tooling matters. Reliability matters. Deployment at scale matters. None of this means AMD or Cerebras won’t win meaningful workloads. I actually expect they will. But there is still a long road between showing impressive hardware and displacing an architecture already running frontier models at scale. And perhaps most importantly: They don’t necessarily need to take NVIDIA’s slice of the pie to succeed. The inference pie itself is getting much bigger. Agentic AI is coming. $NVDA $AMD $CBRS