Multi-Query Attention
Definition
Multi-query attention saves memory by sharing information.
Attention where multiple query heads use a shared key/value head pair to reduce memory requirements and inference bandwidth.
Simply explained
Multiple querying compute units use the same pool of comparison information instead of each holding it twice.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A language model shares K/V states across query heads and reduces the KV cache.
Limits and pitfalls
- Confusing MQA with GQA or ignoring possible quality differences.
Origin & history
Shazeer described multi-query attention in 2019 for fast autoregressive decoders.
Use in mid-sized companies
- Possible example: A language model shares K/V states across query heads and reduces the KV cache.
Frequently asked questions
What does multi-query attention mean?
Attention where multiple query heads use a shared key/value head pair to reduce memory requirements and inference bandwidth.
Which mistakes should be avoided?
Confusing MQA with GQA or ignoring possible quality differences.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: