Multi-Query Attention in Transformers: Faster Decoding in 2026
Updated on January 23, 2026 4 minutes read
Frequently Asked Questions
What problem does Multi-Query Attention solve?
MQA targets the decoding-time bottleneck in autoregressive generation. By sharing keys and values across heads, it reduces KV-cache size and memory bandwidth pressure during token-by-token inference.
Is Multi-Query Attention the same as Multi-Head Attention?
No. Multi-head attention typically uses separate Q/K/V projections per head. Multi-query attention keeps multiple query heads but shares a single set of keys and values across those heads.
Does MQA always improve quality or accuracy?
Not necessarily. MQA is mainly an efficiency optimisation and can involve quality trade-offs depending on the model and task. It’s best validated by running evaluations for your specific use case.