Vision
Problem
- Limited Model Exploration: AI researchers and applications face restrictions due to resource-intensive infrastructure. Outsourcing to LLM API providers alleviates this but limits model variety.
- Unsustainable Open Source Innovation: Independent ML engineers struggle to distribute and monetize their models. Reliance on major infrastructure providers diminishes incentives, hindering sustained innovation.
- Unequal Market Access: Vertically integrated companies prioritize enterprise customers and top-tier models, making affordable inference for mid-tier models harder to find.
Landscape
Centralized AI Inference
The Web3 industry tackles AI decentralization across various layers:
- Infrastructure-as-a-Service Cloud: Akash Network, Ritual, Render, NetMind.AI
- Computing-Resource Marketplaces: io.net, Gensyn, nimble, Kuzco, Morpheus AI
- Model Tokenization and Training: SaharaLabs, Bittensor, MyShell
- AI Agents: SingularityNET, Humans.ai, sensay, ChainGPT, AgentLayer
- Data Tokenization: Synesis One, Grass.io, GagaNode, Ocean Protocol
- AI Applications: inSure DeFi, Sleepless AI, NFPrompt, Hooked Protocol
We operate within the computing-resource marketplace sector but believe current solutions fail to optimize GPU resources and create immediate value for AI consumers.