When Does Local AI Hardware Beat Cloud Models for Coding? Measure Cost per Accepted Change
Rama_Surasani_ · reddit · 2026-09-22
Comparing hardware prices to token spend misses the point: the metric that matters for coding is cost per accepted coding change. The author proposes testing 20–30 real repository tasks and tracking patch acceptance, time to first useful result, context size, cloud token usage, local power consumption, and how often a stronger model is still needed.
Local likely wins for private code, steady workloads, search, summarization and repetitive edits; cloud still wins for bursty usage, hard debugging and large refactors. A hybrid router—local for predictable tasks, escalating only when evaluations fail or complexity exceeds a threshold—could get the best of both.
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