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By 2026, language models have moved off the cloud and onto the device in your pocket. What was a research demonstration two years ago is now a routine engineering capability, and the centre of gravity for artificial intelligence has begun to migrate from distant data centres to local silicon.

The episode traces the four engineering moves that made this possible. Quantization, which shrinks a model by storing its parameters with less precision. Optimized key-value caches, which let a model hold a long conversation without exhausting memory. Neural Processing Units, the dedicated AI accelerators now standard in flagship phones. And specialized frameworks such as LiteRT-LM and llama.cpp, which finally make all three usable from a single application.

The consequences reach further than performance figures. Privacy becomes the default rather than a feature, because data never leaves the device. The cost structure of AI applications changes, because there are no per-query cloud fees. And the link between training capital and deployment capability begins to decouple, opening the door for small teams to ship genuine intelligence on hardware they already control.

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Squeezing AI into your Pocket

Embedded AI - Intelligence at the Deep Edge

Squeezing AI into your Pocket