Who checks the GPU running your AI request? How a decentralized inference network catches hosts that cheat

When you call an AI API, you trust the provider to run the model it advertises. When the GPUs belong to strangers, that trust has to be engineered. Here's how one open-source design approaches it. Disclosure: I contribute to Gonka, the project used as the example. 1. Spot checks instead of full re-runs Re-running every request would double the cost. Instead, a random 1–10% of tasks is re-run by other hosts. No host knows which requests will be checked, so cheating on any request is a gamble. 2. Reputation Hosts that keep passing checks get checked less often A host that fails gets checked more often and loses rewards 3. Open weights as an outside check The models (DeepSeek V4-Flash, GLM-5.3-Flash, MiniMax M2.7) are open-weight, so anyone can compare an endpoint's output with the public model. Closed models can't be checked this way. 4. The unsolved part Different GPUs don't produce bit-identical output, so "did it match?" needs a tolerance, and a clever cheat could try to hide inside it. That's still an open research question. The takeaway Decentralized AI doesn't remove trust. It swaps "trust the company" for probability, reputation and open weights. Whether that's enough is a fair debate. Links Website & docs: gonka.ai Code: github.com/gonka-ai/gonka AMAs & walkthroughs: youtube.com/@Gonka-AI Discord: discord.gg/REcpeYc7P7