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AI Hardware 2026: The Chips Defining the Intelligence Explosion
@garagelab
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2026-05-16 01:06:22
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NVIDIA's H100 and B200 dominance in training large language models isn't accidental — it reflects architectural decisions made years earlier around memory bandwidth and programmability. But the competitive landscape is shifting. Google's TPU v5, AWS Trainium2, AMD MI300X, and Groq's LPU each take different architectural bets on memory, precision, and interconnect. The inference vs training divide is reshaping procurement decisions. And neuromorphic chips from Intel and IBM hint at a post-transformer architecture future where power efficiency matters more than raw FLOPS.
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