Three reasons that justify local inference, three that do not, and an honest account of the capability gap. Plus the hybrid setup most teams end up with.
CUDA lock-in is four layers, not one, and they differ hugely in portability. What is genuinely stuck, what is not, and how to price the alternative.
Multi-GPU scaling disappoints because the interconnect, not the silicon, is usually the limit. When more cards help, and when one bigger card is the answer.
Memory capacity decides whether you can run a model at all, bandwidth decides how it feels, and compute is usually third. A practical sizing guide.
Quantum processors will live in data centres behind an API, not on your desk. Where the hardware really stands in mid-2026, and which skills transfer.
Blackwell, Rubin, Feynman — a new architecture every year. Why research labs should buy for memory and facility fit rather than trying to time the roadmap.
QPUs are being colocated with GPU clusters not to accelerate AI, but because quantum error correction has a microsecond deadline only AI-class silicon can meet.
Post-quantum migration deadlines are set by regulation and data shelf life, not by qubit counts. Here is why the two clocks decoupled, and what to do in the next twelve months.