KV precision and architectural compression

Picture it

If weight quantization shrinks the vast library of a model's brain, KV compression shrinks the scratchpad it uses during a conversation. Imagine multiple readers sharing a single massive reference book; instead of each reader copying the same background chapters into their own notes, they share a compressed summary. Techniques like grouped-query attention pool these shared context notes, freeing up space to host entirely new conversations in the same room.

What to understand

• KV precision, scale calibration, cache capacity, and attention accuracy
• GQA/MQA versus MLA; architecture and backend compatibility

Follow the references

1. 1 · Documentation

   vLLM · Quantized KV cache ↗

   Read accuracy and scaling-factor guidance before enabling low-precision KV.

2. 2 · Paper

   DeepSeek · DeepSeek-V2 ↗

   Use the MLA diagram to contrast latent cache storage with ordinary per-head KV.

Make it concrete

Hold weight precision fixed; compare KV formats supported by the backend. Sweep context length and measure memory, retrieval accuracy, and decode latency.