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The case for running models on the device is made in three clauses, and each represents a different kind of constraint (2:25, 2:34).
Cost, where a local model handling a task well removes a cloud API call. Availability, where the application works on a flight. And data, where requirements prevent information leaving the device at all.
The third is the one that changes what is buildable rather than what is affordable. Cost and offline capability are optimisations. A regulatory or contractual prohibition on transmitting data is a wall, and on-device inference is the only way through it.
The hardware caught up
The reason this is newly practical is unglamorous: improvements across compute targets, including new processor extensions integrated into the runtime (2:51).
Worth stating because on-device AI has been promised for several years and mostly disappointed. What changed is not a modelling breakthrough but silicon and the software that reaches it — instruction set extensions for the operations these models perform, and a runtime that actually uses them.
The reported result is around 30 per cent improvement in image editing functions including subject selection, sky selection and scene enhancement (23:17). Modest as a headline; decisive at the threshold where a feature becomes usable at all.
Small models need adaptation, not scale
The guidance about fine-tuning is precise about where it matters: adapting to a specific problem or domain is particularly important for smaller models, one billion parameters or below (7:57).
This is the practical inversion of the usual advice. Large models are general enough to handle a specific domain with prompting. Small models are not — their capability budget is spent, and getting them to perform on your problem means spending some of it on your problem specifically.
Which reframes what deploying a small model involves. It is not choosing a cheaper option from a menu. It is committing to an adaptation step that the large-model path lets you skip, in exchange for properties the large-model path cannot offer.
The examples are the argument
Two illustrations do more work than the specifications.
A game where characters respond rather than reciting pre-written dialogue trees (8:34). This is not primarily about cost — it is that dialogue generated per interaction cannot depend on a network round trip inside a game loop, so latency alone dictates the model runs locally.
And an acoustic monitor deployed into a forest to track a rare bird's migration (25:07). No connectivity, no power budget for transmission, and the processing has to happen where the microphone is.
Neither is a cheaper version of a cloud application. Both are applications that a cloud model cannot serve at any price, which is the honest case for this whole category.
Key numbers
- ~30%
- reported performance improvement across image editing functions from runtime and silicon work 23:17
Talk chapters
Key takeaways
- 01
Three arguments for local inference — cost, offline availability, and data that cannot leave the device — of which only the third changes what is buildable. 2:34
- 02
What made this practical is silicon and runtime work rather than modelling: processor extensions integrated into the inference runtime. 2:51
- 03
Around thirty per cent improvement is reported across image editing functions including subject and sky selection. 23:17
- 04
Domain adaptation matters most for the smallest models, whose capability budget is already spent and must be redirected toward your problem. 7:57
- 05
An acoustic monitor placed in a forest to track a rare species is the clearest case: no connectivity, no transmission budget, processing at the microphone. 25:07
Entities mentioned
Organizations
Related talks
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