If you're training AI models on Sui, Walrus ensures your datasets stay verifiable end-to-end. Each blob—whether embeddings, fine-tuned weights, or raw training logs—gets a cryptographic ID anchored on Sui, with Merkle proofs confirming integrity and origin. Updates log as immutable events, letting you trace versions without trusting intermediaries. Seal adds programmable encryption: set access rules in Move contracts, like time-locked decryption or role-based views, so collaborators query subsets without exposing full data.
Node setup for storage providers: Delegate stake WAL via dPoS—current network has hundreds of nodes with 4-5x redundancy via Red Stuff encoding (fountain codes variant). Operators earn from fees after 10% delegator cut; burns on stake shifts and penalties keep supply deflationary (max 5B total, 60% community-allocated including 10% subsidies for low-cost epochs). Governance: Propose changes to redundancy ratios or epoch fees (24-hour base) through on-chain votes proportional to stake.
Dev workflow: Use Rust SDK for blob uploads—specify epochs (1-128, ~1 day to 3 months), pay ~0.1 WAL/MB/epoch adjusted by stake levels. Retrieve via aggregators with HTTP endpoints; batch uploads for efficiency, saving 20-40% on gas. Nautilus integration runs confidential inference on encrypted blobs, outputting zk-proofs of computation correctness verifiable on Sui.
Real integrations: RealTBook stores Bookie NFT metadata as blobs for permanent access; AI marketplaces register datasets with licensing terms enforced by Seal, triggering micro-payments on usage. For privacy-focused agents, combine with Nautilus enclaves—process queries off-chain, log receipts on Sui for audits. Testnet tip: Use CLI to simulate local aggregators, upload sample models, verify proofs against devnet chain.
Walrus scales for enterprise: Auditable pipelines pull real-time blobs, run embeddings in secure environments, monetize via programmable royalties. No single failure points—data survives 75% node downtime thanks to erasure shards.