# Google AI Edge: Tiny LMs Powering Robotics & Devices _Google's Cormac Brick discusses the state-of-the-art in tiny AI models, their applications in robotics and edge devices, and the importance of fine-tuning for broad accessibility._ **Published:** 2026-07-25 **Source:** https://www.startuphub.ai/ai-news/artificial-intelligence/2026/google-ai-edge-tiny-lms-powering-robotics-devices --- Cormac Brick, Principal Software Engineer on the AI Edge team at Google, recently spoke at the AI Engineer World's Fair about the growing importance of "tiny" AI models for edge devices and robotics. He highlighted the shift from solely relying on cloud-based AI to enabling intelligence directly on a vast array of devices, from smartphones to IoT gadgets. Edge AI BenefitsDriver From the article 5 mentionsBrick emphasized the core benefits of edge AI, which include reduced latency for faster, more consistent user experiences, enhanced privacy as data remains on the device, reliable offline functionality, and significant cost savings by reducing cloud compute and token usage, especially at scale.but faceDeployment ChallengesDriverDRAM cost and hardware price increases constrain AI on edge devicesFrom the article 3 mentionsDeploying AI on edge devices, however, presents several challenges.solved byTiny AI ModelsCoreGoogle's Cormac Brick discusses state-of-the-art tiny LMs for roboticsFrom the article 9+ mentionsCormac Brick, Principal Software Engineer on the AI Edge team at Google, recently spoke at the AI Engineer World's Fair about the growing importance of "tiny" AI models for edge devices and robotics.Fine-Tuning PowerContextfine-tuning and community models crucial for broad accessibility and applicationsFrom the article 5 mentionsThe approach for tiny models often involves leveraging off-the-shelf models for fixed tasks or fine-tuning models for specific outcomes.Small vs. TinyContextdistinguishing model sizes for optimal performance on constrained edge hardwareFrom the article 9+ mentionsBrick differentiated between "small" models, typically ranging from 1 to 4 billion parameters, and "tiny" models, which can be as small as 50 million parameters.powersRobotics & DevicesEffectenabling intelligence directly on smartphones, IoT gadgets, and various devicesFrom the article 9+ mentionsSmall models, requiring around 4-8GB of RAM for robotics and IoT applications, are already integrated into high-end smartphones and are suitable for devices like laptops.leading toDeveloper TakeawaysOutcomefocus on efficient models and leveraging community for edge AI successFrom the article 2 mentionsBrick concluded with key takeaways for developers focusing on consumer devices and entry-level robotics: ## The Case for Edge AI Brick emphasized the core benefits of edge AI, which include reduced latency for faster, more consistent user experiences, enhanced privacy as data remains on the device, reliable offline functionality, and significant cost savings by reducing cloud compute and token usage, especially at scale. These advantages are critical for a wide range of applications. ## Navigating AI Deployment Challenges Deploying AI on edge devices, however, presents several challenges. Brick identified DRAM cost as a primary constraint, noting that even basic hardware like Raspberry Pis have seen significant price increases. This necessitates a strong focus on model optimization techniques like quantization and the use of the smallest possible models for specific tasks. Furthermore, the wide variety of target devices and the tendency for LLM research to focus on larger models mean that the lower end of the LLM spectrum is less explored. ## Small vs. Tiny Models Brick differentiated between "small" models, typically ranging from 1 to 4 billion parameters, and "tiny" models, which can be as small as 50 million parameters. Small models, requiring around 4-8GB of RAM for robotics and IoT applications, are already integrated into high-end smartphones and are suitable for devices like laptops. However, they may still be out of reach for lower-tier devices. The playbook for small models often involves prompting techniques like zero-shot or LoRA adapters, proving effective for tasks like function calling. Tiny models, on the other hand, are designed for even greater accessibility, requiring less than 2GB of RAM and capable of running on a much wider array of consumer devices. These models are ideal when AI is a secondary feature or when system health and responsiveness are paramount. The approach for tiny models often involves leveraging off-the-shelf models for fixed tasks or fine-tuning models for specific outcomes. ## The Power of Fine-Tuning and Community Models Brick showcased how fine-tuning can significantly improve model performance and reduce resource requirements. He highlighted the "Mobile Actions" model, which, after fine-tuning with synthetically generated data, can reliably convert arbitrary text input into function calls for common mobile tasks. He also pointed to the availability of various community models on platforms like Hugging Face that are optimized for specific tasks such as Automatic Speech Recognition (ASR), vision, and embeddings. The presentation also featured examples of Google's AI Edge tools, including [LiteRT-LM](/ai-news/ai-research/2026/google-s-cormac-brick-on-tiny-llms-for-on-device-agents) and MediaPipe, which simplify the deployment of AI models across edge devices. Brick demonstrated the AI Edge Gallery app, available on iOS and Android, as a practical way for developers to experiment with small models directly on their phones. ## Key Takeaways for Developers Brick concluded with key takeaways for developers focusing on consumer devices and entry-level robotics: - Small LLMs are user-friendly and perform well on NPUs. - Tiny models expand the reach of AI to a broader range of devices, often requiring fine-tuning. - Visual intelligence can be effectively incorporated using tiny VLMs. - Robust voice-to-function calling is achievable by investing in appropriate synthetic datasets for fine-tuning. Ultimately, the trend towards smaller, more efficient AI models is democratizing the deployment of intelligent capabilities across a vast spectrum of devices. --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory.