Excited to announce that we won yesterday's @encodeclub hackathon with our Private Inference project, and the product is already live. Here's why we believe this matters: Current AI providers have centralized control. Companies can change policies, restrict access, or shut down services at any time. The recent Fable-related ban is a good example of how access can be limited when a central authority decides to flip the switch. At the same time, users contribute valuable data that helps improve AI models, but they rarely receive any direct benefit or discounts for doing so. We're building Private Inference, a decentralized network where anyone can access and run local AI models while preserving greater control and privacy. Instead of GPUs sitting idle or being used for proof-of-work mining, node operators can contribute compute power to run AI workloads and get paid by consumers who use the network. Open-source models like @Zai_org GLM 5.2 are already available for free, but running them still requires significant hardware resources. Our goal is to create a distributed compute marketplace that makes these models accessible to everyone while rewarding people who provide the infrastructure. @PinaivuAi chose the @SuiFoundation stack because three Sui-native components solve core problems in decentralized AI inference: Sui (Gasless Stablecoins & Settlement) → @SuiNetwork 's recent support for gasless stablecoin transactions makes decentralized inference payments practical at scale. GPU node operators can receive payouts without needing to maintain a SUI balance for gas, reducing onboarding friction and simplifying participation. Combined with Sui's object-centric architecture, this provides a clean and efficient settlement layer for a global network of independent compute providers. Nautilus → Enables verifiable off-chain infrastructure. The coordinator and chat-relayer run inside @awscloud Nitro Enclaves, and Nautilus allows their attestations to be registered and verified on-chain, turning these services from trusted black boxes into cryptographically verifiable components. @WalrusProtocol (Context Layer) → Consumer inference is fundamentally stateless, and there is no KV-cache sharing between independent GPU nodes. Without a shared memory layer, multi-turn conversations would become tied to whichever node handled the first request, creating hidden centralization.@WalrusProtocol solves this by acting as a decentralized context layer: encrypted conversation history is stored as content-addressed blobs that any node can fetch, decrypt, and continue from. A node serving turn ten does not need to have served turn one. This allows inference routing to remain flexible while preserving conversational continuity. Routing receipts are archived to Walrus as well, creating a tamper-evident audit trail that persists independently of Pinaivu's infrastructure. would love to get feedback on it ?@EvanWeb3 @GDanezis @abhinavg6 @b1ackd0g @kostascrypto @EmanAbio @0xAmoghGupta @SuiCommunity @suidevelopers @DTDaun