Conventional attention has to keep intermediate results for every token it has read, so the KV cache grows with context length and eats GPU memory in proportion. Linear attention compresses that history into one fixed-size state, so the footprint stays the same no matter how long the input runs. The trade-off is weaker exact recall of earlier tokens, which is why recent models interleave it with full-attention layers.