What is Multi-head Latent Attention (MLA), and how does it differ from MQA and GQA?
MQA and GQA cut the KV cache by sharing key/value heads. MLA goes another way: compress K and V into a low-rank latent and cache that instead. What matters is recognizing it as a cache trick rather than a head-sharing trick, and why it preserves quality.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
MQA and GQA cut the KV cache by sharing key/value heads. MLA goes another way: compress K and V into a low-rank latent and cache that instead. What matters is recognizing it as a cache trick rather than a head-sharing trick, and why it preserves quality.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
