How MLA Compresses KV Cache
Abhishekcur · x · 2026-07-08
The post explains how DeepSeek V3 and similar architectures alleviate KV cache pressure during long-context concurrent inference. By using MLA, each layer only caches compressed low-dimensional latent variables and position keys, and then reconstructs K/V during inference. This drastically reduces the per-token caching overhead compared to the high memory usage of traditional MHA.
Related event: How DeepSeek-R1 Achieves Faster, Cheaper Inference(3 posts)→
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