Quantization Dominates Rank Reduction for KV-Cache Compression

A new study demonstrates that quantization significantly outperforms rank reduction for compressing KV caches in transformer inference, achieving 4-364 PPL improvements across multiple models. The research shows that preserving all dimensions while reducing precision is structurally superior to discarding dimensions, with INT4 quantization matching FP16 accuracy while enabling 75% total KV reduction.

This research addresses a fundamental optimization challenge in transformer inference: how to compress the key-value cache without degrading model performance. The comparison between two compression strategies reveals a surprising hierarchy that challenges common assumptions about dimension reduction versus precision loss. Quantization's dominance stems from how transformer attention mechanisms function under softmax routing, where removing dimensions can cause discrete failures in token attention patterns, while quantization noise remains bounded and typically preserves score ordering.

The findings carry significant implications for production deployment of large language models. As models scale from 124M to 14B parameters and beyond, KV cache memory becomes a critical bottleneck for inference throughput and cost. The research demonstrates that INT4 quantization achieves 75% total KV reduction with minimal perplexity penalty (+0.18 PPL on Mistral 7B), translating directly to improved inference speed and reduced hardware requirements. This matters particularly for edge deployment and cost-constrained inference scenarios where memory bandwidth is expensive.

For the AI infrastructure sector, this work provides actionable guidance for optimizing inference stacks. The mathematical formalization showing projection damage exceeds quantization damage by 3 × 2^(2b) per direction gives practitioners principled justification for choosing quantization-first strategies. The basis-ablation result confirming basis-independence spreads findings across different architectural approaches, suggesting broad applicability. As inference optimization becomes increasingly competitive, techniques yielding 75% memory reduction without significant accuracy loss directly impact model serving profitability and accessibility, particularly for open-source deployment infrastructure.

• →Quantization outperforms rank reduction by 4-364 PPL depending on compression level across diverse model sizes
• →INT4 quantization matches FP16 accuracy while rank-32 reduction collapses to 0.4% on LAMBADA benchmark
• →Softmax attention routing creates discrete failure modes when dimensions are removed, while quantization noise remains bounded
• →Joint K+V INT4 quantization achieves 75% total KV reduction with only +0.18 PPL impact on Mistral 7B
• →Findings are basis-independent with spread under 0.4 PPL, indicating broad architectural applicability