The key idea behind Deepseek V4's hybrid attention architecture is not to treat all past information as equally important.

The attention residuals design allows the AI to stay perfectly focused on the most important details by selectively choosing which layers' information to use.

Deepseek V4's sliding window attention keeps the most recent tokens, such as the last 128 words, completely uncompressed with full fidelity.

Deepseek V4's data center choreography breaks down data transfer into smaller sequential waves, overlapping computation and communication to eliminate network latency.

Transformers fixed the amnesia issue by introducing an attention mechanism, allowing the model to look back at any previous word directly and selectively get exactly the information it needed.

Deepseek V4 Pro requires 3.7 times lower FLOPs (compute) compared to the previous Deepseek version 3.2.