A fit calculator answers one question: will these weights load in your memory. A leaderboard answers a different one: is this model good at tests in general. The question that decides what you actually run is neither. It's "will Q4_K_M hurt my invoice extraction accuracy on my machine," and the only reliable way to answer it is to run your extraction on Q4_K_M and measure.
QuantProof automates that. Point it at a folder of real examples from your task. It sweeps every model you already have in Ollama (or whatever a Rapid-MLX server is serving), scores every output with deterministic scorers, measures time to first token, tokens per second, and peak memory, then recommends the smallest quant whose quality stays within 2% of the best measured result.
npm install -g quantproof
quantproof ingest my-tasks.md # a local model drafts the task pack from your notes
# or: quantproof init my-task # scaffold it yourself
