Why KV Cache Matters — How MQA, GQA, and MLA Make LLM Inference Faster

Short summary

KV Cache eliminates redundant Key/Value computation during autoregressive text generation, but cache size becomes the memory bottleneck in production as context grows. MQA, GQA, and MLA optimize this trade-off: MQA shares K/V across all heads for minimal memory; GQA balances sharing per head-group; MLA compresses K/V into latent form. Each technique reduces cache size differently, directly affecting inference latency, GPU memory, batch size, and deployment cost.

• •KV Cache eliminates recomputation but creates memory bottleneck—cache size grows with context length
• •MQA, GQA, and MLA reduce cache size through different strategies: shared K/V, grouped sharing, or compression
• •Cache efficiency directly determines deployable context length and inference cost in production systems

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