The Physics Problem
Edge devices have power budgets measured in watts, not the hundreds of watts a datacenter GPU can draw. Deploying a 7B parameter model on a Raspberry Pi is not just a compression problem — it is a fundamental constraint problem. Thermal throttling alone can cut inference speed by 40% on sustained workloads.
What Actually Works
The teams seeing real edge AI deployments in 2026 are not using general-purpose frameworks. They are using quantization-aware training, purpose-built runtimes like ONNX Runtime with NPU acceleration, and models architected specifically for edge constraints rather than squeezed down after the fact.
The Operational Reality
Edge AI looks elegant in a demo. In production, it means managing model versions across thousands of heterogeneous devices, dealing with hardware failures that corrupt model weights, and building update pipelines that work over intermittent cellular connections. The tooling is getting better, but it is not turnkey yet.