How DeepSeek's Multi-Head Latent Attention Changed the Game

What if you could cut your transformer’s KV cache by over 90% without touching your GPU? In this video, we break down how DeepSeek’s Multi-Head Latent Attention (MLA) completely changes the game for long-context LLMs by aggressively compressing keys and values into a tiny latent space—while keeping model quality essentially unchanged. We’ll start from the real bottleneck: why the KV cache explodes as sequence length grows, and why older tricks like MQA and GQA help on memory but often pay in quality. Then we dive into MLA’s core idea: low-rank compression of K and V into latents, regenerating…