Ok, Nvidia basically owns the GPU accelerator market (+90% market share) , and that’s probably not changing anytime soon. But with AMD releasing its new “Venice” CPUs this week and hitting a massive 46% revenue share in x86 servers (up from literal 0% in 2017) , it feels like the actual battlefield in AI hardware is quietly shifting toward CPU orchestration. Standard LLM queries are passive, but agentic workflows are a different beast. They loop, call tools, query databases, and self-correct continuously. Some research shows agents can consume up to 1,000x more tokens than basic chatbot prompts. While GPUs do the heavy lifting on matrix math, CPUs handle the orchestration, data feeding, context switching, and backend enterprise integrations. If your CPU stalls or chokes on data pipelines, those $30k Nvidia GPUs are just sitting idle waiting for work. AMD is claiming top-end Venice gives 2.2x the performance per core over Nvidia’s comparable Vera processor. This is probably why hyperscalers like AWS, Azure, and Oracle are increasingly ignoring Nvidia’s fully vertically integrated racks (Grace Blackwell / Vera Rubin) and defaulting to a “Best-of-Breed” modular setup: high-core AMD CPUs paired with Nvidia GPUs to optimize their intelligence-per-watt costs. We have now : Nvidia’s vertical integration (CUDA + proprietary networking + own CPUs) VERSUS AMD pushing an open, modular ecosystem where cloud providers mix and match to keep infrastructure costs from exploding. Exciting no ? submitted by /u/remybigot
Originally posted by u/remybigot on r/ArtificialInteligence
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