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Insight for: Engineering findings: K/V norm disparity + MSE > Prod + outlier mixed precision

TurboQuant's quantization strategy, specifically regarding K/V norm disparity, attention quantization methods (MSE vs. Prod), and outlier detection (dynamic vs. fixed).

Analyzed: Apr 1, 2026

This issue presents critical engineering findings for TurboQuant, revealing significant opportunities for optimization. The 'K/V norm disparity' necessitates mixed precision, as uniform quantization catastrophically fails for models like Qwen with high K/V ratios. Furthermore, MSE is empirically shown to outperform the paper's recommended Prod for Attention, yielding dramatically lower PPL. Dynamic outlier detection also offers efficiency gains over fixed allocation. For B2B SaaS, these findings are paramount: improved quantization directly translates to reduced memory footprint and potentially faster inference with minimal quality degradation. Adopting these refinements can significantly enhance the cost-efficiency and performance of LLM deployments, providing a competitive edge in resource-constrained environments.

K/V norm disparity bit budgets mixed precision uniform quantization MSE Prod TurboQuantProd (QJL) Attention PPL softmax Dynamic vs Fixed Outlier Detection outlier.py RMS per-layer dynamic detection 8-bit 3-bit 3.6-bit avg

GitHub Issue

Parent Entity

Engineering findings: K/V norm disparity + MSE > Prod + outlier mixed precision

State: Open