Decentralized storage is used to host AI model weights, and the cost is indeed much lower than traditional cloud services. This approach is crucial for the future of AI infrastructure. However, my practice is: never skip the step of local hash verification.

The reason is very practical. Although erasure coding can protect data security during network transmission and node reorganization, as a final user, blindly trusting the data to be 100% perfect upon restoration is irrational. Especially in adversarial environments, even a single-bit change in the model file can cause the generated results to go awry.

My process is as follows: after downloading the model from distributed storage, first compute the hash locally and compare it with the original fingerprint uploaded. Only if both match exactly do I load the model into VRAM. Cheap storage costs must be exchanged for more rigorous client-side verification. This is not over-caution; it’s a necessary technical safeguard.