Turboquant's global rotation technique improves outlier handling in 3-bit quantization but creates nearly 382,000 spurious activations, revealing a hidden tradeoff in low-bit model compression.

Global rotation spreads outliers during low-bit quantization, improving reconstruction quality and cosine similarity on Qwen-2.5-1.5B at 3-bit precision

Testing revealed 381,999 'ghost activations'—neurons that were silent in FP16 but became artificially active after rotation and quantization

While outlier reconstruction and MSE on large spikes improve with rotation, the technique dramatically worsens sparsity, permanently polluting the semantic noise floor

The tradeoff suggests current quantization methods like Turboquant, Rabitq, and Quip solve one problem while introducing artificial noise that may impact model behavior