---
title: "Why Large? Tiny LMs & Agents on Edge/Robotics"
category: "talks"
date: "2026-06-30"
time: "2:50pm-3:10pm"
track: "Robotics & World Models"
room: "Track 2"
speakers: ["Cormac Brick"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Robotics & World Models"
scheduleRoom: "Track 2"
scheduleLabels: ["Robotics & World Models", "Track 2", "sponsor", "confirmed"]
---
# Why Large? Tiny LMs & Agents on Edge/Robotics

## Conference Context
- Date/time: 2026-06-30 · 2:50pm-3:10pm
- Track/room: Robotics & World Models · Track 2
- Speaker(s): Cormac Brick
- Session type/status: sponsor · confirmed

- Track: Robotics & World Models
- Room: Track 2
- Session type: sponsor
- Status: confirmed

## Session Description
big models get a lot of press. small model scale much better. RAM is expensive. The real world needs tiny models for scale on the edge. This workshop will cover how to combine both for mobile and robotics deployment. specifically covering: - skills are different on mobile - tiny LLMs <1B scale much further on mobile/web - how to fine tune and train tiny models. - skills on robotics / edge/ mobile - latest open models for edge (including gemma, qwen, and anything else that happens in next 10 weeks) This talk will focus on open models, including some gemma variants that will be shortly announced.

## Media Evidence
[From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google](https://www.youtube.com/watch?v=-TiET_K-E_g) (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

- Source video: `youtube--TiET_K-E_g`
- Slide deck: [[youtube--TiET_K-E_g-dense-slides|Dense Slides: From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google]] — 1 visible slide image(s); 1 HTML recreation(s).
![[assets/dense-slides/-TiET_K-E_g/slide-001.jpg]]
- Additional slide evidence: [[youtube--TiET_K-E_g-slides|Slides: From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google]], [[youtube--TiET_K-E_g-reconstructed-slides|Reconstructed Slides: From 46% to 90%: Fine-Tuning Tiny LLMs for On-Device Agents — Cormac Brick, Google]]
- Slide-derived themes for `youtube--TiET_K-E_g`: running, edge, many, benefits, faster, network, involved, sensitive.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
- `youtube--TiET_K-E_g` — 5 slide-derived text signals
- Slide-derived themes for `youtube--TiET_K-E_g`: running, edge, many, benefits, faster, network, involved, sensitive.
- Evidence links for `youtube--TiET_K-E_g`: [[youtube--TiET_K-E_g]], [[youtube--TiET_K-E_g-slides]], [[youtube--TiET_K-E_g-dense-slides]], [[youtube--TiET_K-E_g-reconstructed-slides]]

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

## People
- [[cormac-brick]]

## Supporting Slides
- [[youtube--TiET_K-E_g-slides]] — extracted from the related public AI Engineer video.

## Slide Evidence
- Slide-only cropped deck: [[youtube--TiET_K-E_g-dense-slides]] (1 viable slide images).
- Related slide/OCR pages:
- [[youtube--TiET_K-E_g-dense-slides]]
- [[youtube--TiET_K-E_g-reconstructed-slides]]
- [[youtube--TiET_K-E_g-slides]]
- Slide-derived terms: `engineering`, `future`, `skills`, `tlms`, `engineer`, `edge`, `example`, `restaurant`, `roulette`, `tral`, `braintrust`, `workos`, `openal`, `tinyllmsand`, `agentsonedgedevices`, `bringing`, `state-of-the-artagenticskilstothe`, `edgewithopenmodels`

## Synthesis
### Synthesized Breakdown
# Why Large? Tiny LMs & Agents on Edge/Robotics ## Conference Context - Date/time: 2026-06-30 · 2:50pm-3:10pm - Track/room: Robotics & World Models · Track 2 - Speaker(s): Cormac Brick - Session type/status: sponsor · confirmed - Track: Robotics & World Models - Room: Track 2 - Session type: sponsor - Status: confirmed ## Session Description big models get a lot of press. small model scale much better. RAM is expensive.

### Speaker And Company Context
- [[cormac-brick|Cormac Brick]] — Principal Engineer, Google AI Edge at [[google|Google]].

### Topics Covered
- [[agent-security]]

### Derived Links And Source Material
- [[youtube--TiET_K-E_g]] — related YouTube source page.
- [[youtube--TiET_K-E_g-slides]] — slide evidence.
- [[youtube--TiET_K-E_g-reconstructed-slides]] — slide evidence.
- [[youtube--TiET_K-E_g-dense-slides]] — slide evidence.

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.