Compute Reservations

Compute Reservations

USDAI’s integration with GPU.NET’s marketplace allows users to reserve and manage GPU compute resources efficiently, addressing both short-term and long-term needs with flexibility and precision.

Pre-Booking: Securing Resources Ahead of Time

• Purpose: Users can lock USDAI to pre-book GPU resources, ensuring availability during high-demand periods like AI research deadlines or product launches.

• How It Works: Through Dapp.gpu.net, users commit USDAI to reserve compute capacity for a future date. The system locks the tokens, allocating resources from GPU.NET’s provider pool based on availability and pricing.

• Example: An AI startup anticipates a surge in model training needs during a hackathon. They lock 200 USDAI to secure 200 GPU-hours a month in advance, avoiding last-minute shortages or inflated costs.

• Benefits: Pre-booking with USDAI guarantees access, mitigates supply risks, and provides cost certainty, critical for time-sensitive projects.

Dynamic Scaling: Real-Time Compute Adjustments

• Purpose: USDAI credits allow users to adjust compute allocations dynamically, scaling resources up or down in response to real-time needs without delays.

• How It Works: Users burn USDAI to access additional GPU capacity instantly or release unused credits back to the network, with allocations managed via smart contracts on GANChain or Solana.

• Example: A gaming company rendering real-time graphics for a live event starts with 50 USDAI worth of compute. As player demand spikes, they add 20 USDAI to scale up GPU resources seamlessly, then scale down post-event.

• Benefits: Dynamic scaling ensures efficient resource use, minimizes waste, and adapts to fluctuating workloads—ideal for applications like live AI inference or on-demand HPC.