AI’s GPU obsession blinds us to a cheaper, smarter solution

GPUs dominate the AI landscape, but CPUs remain an untapped resource capable of powering diverse AI tasks efficiently. Decentralized compute networks could save costs and scale AI infrastructure in smarter ways.

Opinion by: Naman Kabra, co-founder and CEO of NodeOps Network

Graphics Processing Units (GPUs) have become the default hardware for many AI workloads, especially when training large models. That thinking is everywhere. While it makes sense in some contexts, it’s also created a blind spot that’s holding us back.

GPUs have earned their reputation. They’re incredible at crunching massive numbers in parallel, which makes them perfect for training large language models or running high-speed AI inference. That’s why companies like OpenAI, Google, and Meta spend a lot of money building GPU clusters.

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