Decentralized Compute

Training and running large models requires enormous GPU capacity. Today most of that capacity sits in hyperscaler data centers — AWS, Google Cloud, Azure — with hourly billing and geographic concentration. Decentralized compute networks try to turn idle consumer and datacenter GPUs into a open market.

Projects like Render, Akash, io.net, and Bittensor-style subnets each emphasize different tradeoffs — rendering vs general compute vs inference-specific routing. Token incentives bootstrap supply early; sustainable demand requires prices competitive with cloud spot instances.

For builders, the key question is not 'is it decentralized' but 'does it meet my latency, uptime, and cost SLA?' Many dApps use decentralized compute for non-critical batch jobs while keeping user-facing inference on managed APIs.