@arnomdul
Web3 storage can support large AI training datasets, but with trade-offs. Protocols like IPFS, Arweave, and Filecoin offer decentralized, persistent storage—great for sharing and verifying large datasets publicly. However, challenges remain: bandwidth limitations, retrieval speed, and cost efficiency at scale may not match centralized cloud services like AWS or GCP. For static, open datasets or archival purposes, Web3 storage is ideal. But for high-frequency model training workflows needing fast I/O, hybrid setups are often more practical—store metadata or hashes on-chain and large blobs off-chain or on traditional systems.
0 reply
0 recast
0 reaction