While often discussed for privacy, running models on-device eliminates API latency and costs. This allows for near-instant, high-volume processing for free, a key advantage over cloud-based AI services.

Don't use your most powerful and expensive AI model for every task. A crucial skill is model triage: using cheaper models for simple, routine tasks like monitoring and scheduling, while saving premium models for complex reasoning, judgment, and creative work.

Advanced AI architectures will use small, fast, and cheap local models to act as intelligent routers. These models will first analyze a complex request, formulate a plan, and then delegate different sub-tasks to a fleet of more powerful or specialized models, optimizing for cost and performance.

The trend for language models is diverging: massive models in the cloud and smaller models (SLMs) at the edge. These SLMs, while lacking the broad knowledge of their larger counterparts, are highly effective when fine-tuned for specific domains and specialized data, making them ideal for device-level intelligence.

The recent economic push for AI to demonstrate a clear return on investment is not new to the edge AI space. Edge applications have always been driven by strict cost and productivity constraints, fostering a culture of rational, value-focused development that the broader AI world is now adopting.

For low-latency applications, start with a small model to rapidly iterate on data quality. Then, use a large, high-quality model for optimal tuning with the cleaned data. Finally, distill the capabilities of this large, specialized model back into a small, fast model for production deployment.

The trend toward specialized AI models is driven by economics, not just performance. A single, monolithic model trained to be an expert in everything would be massive and prohibitively expensive to run continuously for a specific task. Specialization keeps models smaller and more cost-effective for scaled deployment.

A cost-effective AI architecture involves using a small, local model on the user's device to pre-process requests. This local AI can condense large inputs into an efficient, smaller prompt before sending it to the expensive, powerful cloud model, optimizing resource usage.

Instead of streaming all data, Samsara runs inference on low-power cameras. They train large models in the cloud and then "distill" them into smaller, specialized models that can run efficiently at the edge, focusing only on relevant tasks like risk detection.