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.