gqa
AI, ML & GenAI interview questions tagged gqa, across every topic.
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Concepts behind "gqa"
The curriculum that explains the ideas these questions test.
Core
Attention Variants: MHA, MQA, and GQAMulti-head attention gives every query head its own key and value heads, which is expressive but leaves the KV cache large and memory-bandwidth hungry at decode time. Multi-query attention shares one key-value head across all query heads to shrink the cache sharply, and grouped-query attention sits between them by sharing key-value heads across small groups. AI, ML, and GenAI engineer interviews probe this because it is the cleanest example of trading model quality against serving memory and throughput, and it explains why frontier models standardized on GQA.🧠 Foundations of LLMs & GenAISign in
Core
The KV CacheIn autoregressive decoding a model would recompute attention over the whole history at every step; the KV cache keeps each token's key and value vectors so a new token only attends and never recomputes. Compute is saved, but the cost shifts to memory: the cache grows with sequence length times batch size and usually turns into the binding constraint in serving. AI, ML, and GenAI interviews probe it because it explains why long contexts are costly to serve, why throughput (not model speed) is often the limit, and why MQA/GQA and PagedAttention exist.🧠 Foundations of LLMs & GenAISign in