3 posts tagged with "edge-computing"

In under two years, on-device language models went from research curiosity to mainstream product feature. Smartphones can now run language models with up to 47 billion parameters. Flagship NPUs have crossed the 100 TOPS threshold. Multimodal models process text, images, audio, and video without cloud connectivity. And the first frameworks enabling actual on-device fine-tuning have arrived.

This guide covers the full arc from early 2025 through February 2026: optimization techniques, hardware capabilities, model releases, inference frameworks, performance benchmarks, commercial deployments, and the emerging frontier of on-device training. It is the most comprehensive single resource on mobile LLM inference available today.

February 2026

Technology paradigm shifts do not arrive as single breakthroughs. They arrive as convergences -- multiple independent trends, each advancing on its own trajectory, reaching a critical density at the same moment in time. The PC revolution required cheap transistors, graphical interfaces, and spreadsheet software simultaneously. The mobile revolution required capacitive touchscreens, 3G networks, and app distribution simultaneously. Cloud computing required virtualization, broadband ubiquity, and pay-per-use billing simultaneously.

We are now witnessing a convergence of equal magnitude. Seven independent vectors -- in hardware, software optimization, regulation, economics, device proliferation, application architecture, and developer infrastructure -- are aligning toward a single, unavoidable conclusion: the future of AI inference is on-device, and the future of AI improvement is federated.

This paper traces each vector with specificity, projects where each leads, and demonstrates why their intersection creates one of the largest platform opportunities in the history of computing.

The irony of modern federated learning: We want to train sophisticated models on edge devices, but those same devices often can't even run the models.

A state-of-the-art language model has 7B+ parameters (~28 GB at 32-bit). An iPhone 15 Pro has 8 GB RAM. This math doesn't work.

Model compression techniques—quantization, pruning, low-rank adaptation—are not just optimizations; they're prerequisites for production FL on edge devices. This post explores cutting-edge compression methods and how Octomil makes them accessible.