The three silent killers in edge AI deployment, and how to catch them before they catch you.

You've done everything right. You trained the model. You quantized it down to INT8. You ran it through your benchmark suite on your dev machine — latency looks great, memory usage looks fine. You're confident. Then you flash it to the Raspberry Pi CM4. SSH in. Run inference. RuntimeError: Failed to allocate memory for output tensors. Requested: 412MB. Available: 380MB. Sound familiar? This specific failure — and the hours of debugging that follow — is almost entirely preventable. But it keeps happening, to experienced engineers, on mature models, in production deployments, because the tools most of us use to validate AI models were built for the cloud, not the edge. This post is about the three root causes of edge deployment failures, why they're so easy to miss, and what a proper pre-deployment profiling workflow looks like. The Gap Nobody Talks About The ML tooling ecosystem has gotten extraordinary at one half of the deployment pipeline: training, fine-tuning, evaluation, serving at