Training vs inference: Training is best done by centralized companies. Training needs dense amount of computing. Training data on a single source = better outcome inference - running the model.
Decentralized compute models - cheaper, faster, and scalable.
Faster -> if you are going to generate a shit load of inference at one go

Latency for decentralized networks (time for a request to be received: you can get the node doing the inference closer to the user vs AWS DS which might be far away. i.e compute at edge.

Decentralized networks have way more supply than demand (GPU): Easy to use, developers take the least amount of time to integrate. Lower barrier of entry = more users = more usage = more demand.

Limited access to high-quality GPUs is not a problem for decentralized networks because it focuses on inference, not training, and doesn't require high-end GPUs.

Monetization: Decentralized networks are cheaper, faster, and scalable for businesses that want to build on top of it. GPU providers provide idling compute thus earning $. However, this only works if there is demand (high utilization of GPUs in the network), therefore networks need to be built so that businesses want to use them to create demand (easy to integrate). The assumption is that AI will eventually be built into everything (Demand IRL). GPU providers also need to be incentivized sustainably to continue to provide compute.

Training vs inference: Training is best done by centralized companies. Training needs dense amount of computing. Training data on a single source = better outcome inference - running the model.
Decentralized compute models - cheaper, faster, and scalable.
Faster -> if you are going to generate a shit load of inference at one go

Latency for decentralized networks (time for a request to be received: you can get the node doing the inference closer to the user vs AWS DS which might be far away. i.e compute at edge.

Decentralized networks have way more supply than demand (GPU): Easy to use, developers take the least amount of time to integrate. Lower barrier of entry = more users = more usage = more demand.

Limited access to high-quality GPUs is not a problem for decentralized networks because it focuses on inference, not training, and doesn't require high-end GPUs.

Monetization: Decentralized networks are cheaper, faster, and scalable for businesses that want to build on top of it. GPU providers provide idling compute thus earning $. However, this only works if there is demand (high utilization of GPUs in the network), therefore networks need to be built so that businesses want to use them to create demand (easy to integrate). The assumption is that AI will eventually be built into everything (Demand IRL). GPU providers also need to be incentivized sustainably to continue to provide compute.