NVIDIA unveiled the Blackwell Ultra architecture at Computex 2026, positioning it as the definitive engine for generative AI training and inference. Built on TSMC's 4NP process, the chip packs 208 billion transistors and pairs with next-generation HBM3e memory, pushing bandwidth to 4.8 TB/s.
On paper, a single DGX B200 system now delivers up to 72 petaFLOPS of FP8 compute. But the market's excitement has less to do with raw teraflops than with the software moat NVIDIA keeps widening: CUDA, TensorRT-LLM, NeMo, and the new NIM microservices are turning hardware advantages into deployable enterprise AI.
In this deep dive, we look past the keynote slides at the supply chain bottlenecks, the winners and losers, and what the new platform means for founders and investors.
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