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# The Hardware That Makes AI Possible

**[Towards Data Science](https://daily.dev/sources/tds)** · 6 min read · 0 upvotes · 0 comments

## Summary

Modern AI workloads require specialized hardware beyond traditional CPUs. GPUs, originally built for graphics rendering, became the backbone of deep learning due to their massively parallel architecture. Google's TPUs take specialization further, optimizing specifically for tensor operations used in neural network training. NPUs bring energy-efficient AI inference to consumer devices like smartphones. Each processor type fills a distinct role: CPUs handle orchestration, GPUs power large-scale training, TPUs accelerate cloud-based ML workloads, and NPUs enable on-device inference with minimal power consumption.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/the-hardware-that-makes-ai-possible>

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Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#gpu](https://daily.dev/tags/gpu)

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