Use Case 1: AI Model Inference Tasks

In modern AI applications, the inference stage—where trained models are called to perform tasks—is often the most operationally intensive part. Unlike training, which is periodic and centralized, inference happens continuously and often at the edge, with high demands on speed and cost-efficiency.

TechTide offers a decentralized, lightweight approach to AI inference. It allows model owners to deploy callable models into the network’s task pool, which are then executed by eligible nodes during their idle cycles.

Task Types Supported:

• Language model inference (e.g., Llama, BLOOM, Mistral variants)

• Image generation (e.g., partial tasks in Stable Diffusion pipelines)

• Image classification and detection (lightweight CNN/ViT models)

• Recommendation and ranking tasks

• Lightweight Q&A or semantic matching models

Execution Flow:

• Task request is submitted, including input data, model parameters, and desired output format;

• Scheduler matches task to eligible nodes, prioritizing those with available GPU resources;

• Nodes perform inference locally and return output;

• Optional multi-node validation ensures consistency and deters manipulation.

Key Characteristics:

• Independent of centralized AI hosting services or cloud vendors

• Leverages idle compute from browser and desktop nodes

• Runs entirely within WebAssembly sandboxes for security and isolation

• Model owners can control usage frequency, access rights, and task-based fees

• Reward points are allocated to nodes based on successful execution and can be converted into $TTD

This design enables low-cost, elastic inference capacity at the edge—especially valuable for projects that need scalability without heavy infrastructure investment.