Compressing model weights is mandatory for edge deployments and cost-effective cloud serving. Yet blind quantization using uniform bit-width reduction often degrades reasoning capability in subtle, hard-to-detect ways. Evaluating loss solely on general perplexity benchmarks masks severe regressions in structured code generation and mathematical output.
Per-Layer Sensitivity and Outlier Retention
Not all transformer layers react equally to bit precision compression. Key-value projection layers and down-projection matrices tend to contain high-magnitude outlier activations that collapse under naive integer quantization. Preserving precision in outlier-heavy layers while aggressively compressing residual blocks maintains task accuracy without bloating memory consumption.
Establishing Domain Specific Quantization Baselines
Before deploying scalar-quantized models into production pipelines, run task-specific evaluation datasets that test edge-case inputs. Automated regression testing should monitor execution accuracy on code syntax and structured response schema adherence alongside raw token throughput. A lower memory footprint offers no value if downstream parsing failure rates spike.