grouped-query kv cache

**Grouped-query KV cache** is the **attention approach where query heads are partitioned into groups that share key-value heads, balancing efficiency between full multi-head attention and MQA** - it offers a practical quality-performance middle ground. **What Is Grouped-query KV cache?** - **Definition**: GQA architecture with multiple query groups mapped to fewer shared K and V heads. - **Design Intent**: Retain more expressiveness than MQA while reducing KV memory overhead. - **Cache Behavior**: KV size scales with group count instead of full query-head count. - **Inference Role**: Common in modern LLM checkpoints optimized for serving. **Why Grouped-query KV cache Matters** - **Efficiency Balance**: Provides strong latency and memory savings with limited quality loss. - **Deployment Flexibility**: Group count can align model behavior with hardware constraints. - **Throughput Gains**: Reduced KV footprint enables higher concurrent decode workload. - **Quality Retention**: Often preserves more accuracy than extreme shared-KV settings. - **Production Stability**: Predictable cache growth simplifies capacity planning. **How It Is Used in Practice** - **Group Configuration**: Select group size during model design or checkpoint choice. - **Serving Calibration**: Tune scheduler and batch sizes for GQA memory-access patterns. - **Regression Testing**: Track quality and latency across different context lengths and tasks. Grouped-query KV cache is **a widely adopted compromise for scalable decode performance** - GQA helps teams balance model quality with practical serving efficiency.