Deploying large language models on edge hardware requires aggressive model compression to fit strict memory budgets. Post-training quantization techniques like AWQ and GPTQ convert weights from 16-bit floating point down to 4-bit integers with minimal loss in general perplexity scores. However, aggregate perplexity often hides severe functional regressions in code generation and structured schema output.
Perplexity Metrics versus Functional Correctness
Standard evaluations often rely on language modeling perplexity to claim near-lossless quantization. In our testing across coding benchmarks and JSON schema extraction, 4-bit models showed a disproportionate failure rate in syntax formatting compared to their 8-bit counterparts. Minor precision loss in attention key projections can cause cascading errors during deterministic output generation.
Identifying Sensitivity Across Model Layers
Not all layers in a transformer network tolerate precision reduction equally. Weight sensitivity analysis reveals that initial embedding layers and final output projections suffer the highest degradation under uniform quantization. Selective precision strategies—keeping sensitive layers at 8-bit while quantizing intermediate feed-forward blocks to 4-bit—preserve structural accuracy with only marginal memory expansion.
Deployment Strategies for Edge Pipelines
Engineers preparing on-device runtimes should prioritize mixed-precision quantization matrices over aggressive uniform compression. Running automated regression test suites against target JSON outputs before deployment prevents silent runtime failures in production applications. Balancing precision per layer guarantees both efficient VRAM utilization and reliable operational behavior.