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Multi-Head Latent Attention (MLA) in DeepSeek-V3 reduces memory usage by over 80% compared to traditional Multi-Head Attention by storing compressed key-value representations instead of full matrices. The technique uses low-rank compression and dynamic reconstruction, enabling 128,000 token context windows while maintaining computational efficiency. MLA achieves massive memory savings for long sequences without significantly increasing compute costs.

How Multi-Head Latent Attention (MLA) Reduces Computational Cost in DeepSeek-V3

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