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ChinaTalk argues that AI chips are moving beyond a single architecture for every job: training, prefill and decode can favour different silicon designs.
Why it mattersThe article says inference decode is memory-constrained and that HBM once looked essential, but the market has matured. It is a useful reminder that chip comparisons depend on what the hardware is being asked to do, not just a headline speed figure.
Discuss: Which matters more when choosing AI chips: one versatile design, or hardware tuned for each stage of inference?
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