Free Lesson
On-Device LLMs: Ship Models to the Edge in 2026
30 min
Aug 4, 2026 1:30 PM
Virtual (Zoom)
In this video
What you'll learn
Know when local beats a frontier API
Score a task on latency, cost, privacy and offline needs to decide if it belongs on-device at all.
Quantize a model without wrecking quality
See what 4-bit and 8-bit quantization cost in accuracy, and where the quality cliff actually falls.
Ship an edge model to real hardware
Run a small model locally with llama.cpp or WebGPU and measure tokens per second on device.
Why this topic matters
Not every feature needs a frontier API. A quantized small model on-device or at the edge can win on latency, cost, privacy and offline use for a real class of tasks, yet most teams reach for the biggest model by reflex. This lesson shows where local wins, where the quality cliff falls, and how to ship one to real hardware. You leave with a right-sizing rule and a runnable local setup.
