3. Use Cases & Application Domains

Scalable AI Inference & Training

• Distributed inference execution for LLMs and vision models

• Federated learning support for privacy-preserving AI

• AI fine-tuning using hybrid edge and datacenter compute

🧩 Tokenized Compute Access & Ownership

• Invest in fractional GPU ownership through RWA tokens

• Receive performance-based yield from real AI workloads

• Participate in compute allocation, task scheduling, and governance

🌐 Edge-Aware Cloud Workloads

• Run workloads closer to users via DePIN edge

• Enable latency-sensitive applications like autonomous vehicles, robotics, and AR

• Dynamic load balancing and fault-tolerant redundancy

🔏 Verifiable AI with Decentralized Validation

• Combat hallucination and bias in LLMs via multi-party validation

• Ensure model integrity through consensus attestation

• Log and prove compute events for auditability