Visualized: CPU vs GPU vs TPU vs NPU vs LPU Architectures in AI
Roger_M_Taylor · x · 2026-07-24
A visual breakdown of the five core hardware architectures powering modern AI, highlighting their fundamental tradeoffs:
- CPU: Built for general-purpose computing with a few powerful cores and deep cache hierarchies. Great for complex logic and OS tasks, but inefficient for repetitive math like matrix multiplications.
- GPU: Dominates AI training by spreading work across thousands of smaller cores executing the same instruction, mapping perfectly to neural network math.
- TPU: Takes specialization further with a grid of multiply-accumulate units designed specifically for tensor operations.
Note: The post quotes a separate article on optimizing KV Cache management in LLMs, claiming a modern caching architecture can cut input token costs by 90% and speed up inference by up to 14x.
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