One of the clearest breakdowns of the hardware powering modern AI. Most people talk about models.
The ones who stand out understand what runs them. GPUs train and run large models
- TPUs scale deep learning on Google’s stack
- CPUs coordinate and run general tasks
- NPUs / APUs power efficient on-device and edge AI
- ASICs / FPGAs accelerate specialized workloads
- Quantum, in-memory, MRAM, neuromorphic point to what’s next
Why this matters?
- Helps you filter signal from buzzwords
- Clarifies what is practical vs hype
- Helps you think in terms of cost, scale, and performance
- Makes conversations with builders and vendors sharper
You don’t need to be a hardware engineer. Just informed enough to navigate where this is all heading.
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