Transformer Breakthrough: Simplified Projections Cut Memory 87% Without Performance Loss

This research fundamentally challenges the necessity of three separate QKV projections in Transformers, proving that unified or shared projection approaches maintain performance while dramatically slashing memory requirements. For edge device deployment, achieving 87.5% KV cache reduction with minimal quality degradation makes running capable AI models on resource-constrained hardware practical. The findings reveal that attention operates in low-rank regimes where keys and values occupy similar representational spaces—a critical insight for designing efficient AI systems.