What people build on SolanaLM.
The same protocol serves several distinct jobs: keeping prompts confidential, turning idle GPUs into revenue, training across data that can't move, and swapping in for a centralized OpenAI endpoint. Each use case is its own page below.
Privacy-preserving inference
Route confidential prompts through onion circuits so no single node sees both source and content.
DePIN GPU compute that earns
Put idle DePIN or spot GPUs to work as an OpenAI-compatible inference node — dual SOL revenue from inference requests and federated training rounds.
Federated model training
Coordinate training across nodes that never share raw data, with FedAvg, FedProx, FedAdam, and SCAFFOLD.
Machine-payable inference for agents
Give an autonomous agent an OpenAI-compatible endpoint it can pay for per call in SOL — point the existing SDK at the gateway, keep your app code unchanged.
Not sure which fits?
Compare SolanaLM against the alternatives, or just run the quickstart and try it.