Stop Trusting Leaderboards: The Hidden Costs of Model Quantization

Leaderboard scores are a poor predictor of how quantized models perform in real-world agent systems, where tool-calling accuracy can collapse even if benchmarks look good. Developers often unknowingly cripple their agents by aggressively compressing models to fit VRAM constraints without measuring the actual performance impact. QuantaMind's Quant Audit tool shifts focus from 'what's the smallest model that runs' to 'what's the largest quantization that preserves reasoning capability.'