AI · 2h ago
KV Cache, MQA, GQA, and MLA: How LLMs Speed Up Inference
KV Cache stores previously computed Key and Value tensors to avoid recomputation during autoregressive generation. This reduces repeated work but shifts the bottleneck to memory as context grows. Techniques like Multi-Query Attention (MQA), Grouped-Query Attention (GQA), and Multi-Head Latent Attention (MLA) further shrink cache size by sharing or compressing K/V tensors.
Meridian48 take
The article explains a core optimization clearly, but practitioners should note that these trade-offs become critical at scale, especially for long-context applications.
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Why KV Cache Matters — How MQA, GQA, and MLA Make LLM Inference Faster →
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