Here's the schema — a single universal rubric you can apply to score any local-AI candidate (a GPU, a model, a serving framework, a deployment) against the same structural skeleton, so every comparison in this whole conversation collapses into one repeatable due-diligence instrument rather than nine separate narratives.
Each row follows the pattern Feature :: Function :: Spec/Metric :: Value(Range/Band/Qty) :: Similar/Related/Other. "Feature" is the atomic attribute being measured (it should not overlap with any other row — that's the MECE discipline). "Function" is what that attribute actually does for you operationally. "Spec/Metric" is the measurable unit. "Value" is the realistic 2026 band, not a single number, since real hardware/software spans a range. "Similar/Related/Other" cross-links the attribute to adjacent decisions so you see what it trades off against. This mirrors the discipline used in vendor-neutral edge-AI scoring: define criteria once, weight them p