How EdgeAIStack sizes edge AI deployments.
Every recommendation is produced by deterministic sizing engines working from a structured hardware and benchmark dataset — not by a language model guessing. This page explains where the numbers come from and how much to trust them.
Data sources
Platform specifications come from vendor datasheets first, cross-checked against community measurements where available. Inference throughput figures prefer vendor-published benchmarks (for example Ultralytics YOLO11 TensorRT results) and are labeled by provenance. All figures are approximate and dated — most recently reviewed mid-2026 — and stream-capacity numbers assume the stated resolution, frame rate, precision, and runtime rather than best-case marketing claims.
Validation
The engines are tested by consistency suites that run thousands of input combinations and assert cross-engine invariants — for example, that a power-optimized recommendation never selects a higher-power platform when a feasible lower-power one exists, or that storage, bandwidth, and power outputs agree with each other for the same workload. Suites run at three levels: individual engines, orchestrated engine groups, and the full System Designer.
Confidence labels
Recommendations carry a confidence label (high, medium, low) rather than a raw score. The label reflects benchmark coverage for your specific workload: measured vendor benchmarks rate higher than interpolated estimates, which rate higher than theoretical TOPS-based extrapolation. When the engines fall back to an estimate, the assumptions panel says so explicitly.
Questions about the numbers?
Every engine result lists its assumptions. If something looks wrong, use the feedback link in the footer — corrections with sources are especially welcome.