The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally
How to spend extra precision budget when shrinking AI models
When AI models are compressed to run faster and cheaper, the damage isn't concentrated in a few critical layers—it spreads across roughly half the model's layers. Counterintuitively, applying finer-grained compression uniformly across the entire model recovers accuracy better than surgically fixing the layers that seem most broken, outperforming targeted repairs by 21–52 percentage points.
As companies deploy larger language models on phones, servers, and edge devices, quantization compression is essential to cut costs and latency. This finding changes how engineers should allocate limited precision budgets: instead of hunting for problem layers, spreading improvements evenly across the model works better in practice and is simpler to implement. This could reduce the engineering overhead of tuning quantization for each new model.