multi-query kv cache

**Multi-query KV cache** is the **attention design where multiple query heads share a single set of key and value heads to reduce KV cache size and memory bandwidth** - it is widely used to improve inference efficiency at scale. **What Is Multi-query KV cache?** - **Definition**: MQA architecture with many query projections but shared K and V representations. - **Memory Effect**: Greatly shrinks KV cache growth relative to full multi-head attention. - **Serving Impact**: Lower KV size reduces memory traffic during decoding. - **Tradeoff Profile**: Efficiency gains may come with quality differences depending on model and task. **Why Multi-query KV cache Matters** - **Throughput Improvement**: Smaller cache and bandwidth needs increase request concurrency. - **Latency Reduction**: Decode steps run faster when KV reads are lighter. - **Hardware Fit**: MQA helps deploy larger models on constrained GPU memory budgets. - **Cost Efficiency**: Lower per-token resource usage improves serving economics. - **Scalability**: Supports high-traffic workloads with predictable memory behavior. **How It Is Used in Practice** - **Model Selection**: Choose MQA-capable checkpoints validated for target quality requirements. - **Kernel Tuning**: Optimize decode kernels for shared-KV access patterns. - **Quality Benchmarking**: Compare MQA and non-MQA variants on domain-specific evaluation tasks. Multi-query KV cache is **a high-impact architecture choice for efficient LLM inference** - shared-KV designs provide substantial serving gains when quality remains acceptable.