Background Compute & Training
EdgeMob extends beyond real-time inference by enabling background AI compute tasks on mobile devices. This includes batch processing, fine-tuning, and retraining, allowing the platform to support a broader range of AI workloads beyond simple model serving.
Batch Processing
Mobile devices can execute batch inference jobs during idle periods (e.g., overnight charging or downtime).
Suitable for workloads such as document analysis, log summarization, or large dataset processing.
Distributes tasks across multiple devices for faster turnaround times.
Fine-Tuning
EdgeMob enables developers to perform lightweight fine-tuning of models on-device or across a distributed set of devices.
Supports scenarios where models need to be adapted to specialized datasets without full retraining.
Privacy-preserving fine-tuning ensures sensitive datasets stay local to devices.
Retraining & Federated Learning (Future Roadmap)
Long-term, EdgeMob supports federated training approaches, where updates to models are trained on-device and aggregated securely.
This allows global models to improve without exposing private data.
Ideal for domains like healthcare, finance, or personalized assistants where user data cannot leave the device.
Benefits
Resource Efficiency: Utilizes idle compute power on billions of smartphones.
Scalable Capacity: Collective background jobs across a network rival traditional cluster performance.
Cost Savings: Removes reliance on centralized GPU farms for smaller retraining tasks.
Example Use Cases
Running batch inference for financial risk models overnight.
Fine-tuning an open-source LLM for domain-specific customer support.
Retraining speech recognition models locally to adapt to individual accents.
By supporting background compute, fine-tuning, and retraining, EdgeMob transforms from a simple inference layer into a full mobile AI infrastructure, capable of powering continuous improvement and adaptation of AI models across the network.
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