What does “Flash Attention” mean?

A managed system for storing and serving validated ML features consistently for training and inference.

The core training algorithm that updates model weights by propagating prediction errors backward through the network.

Reducing numeric precision of model weights to decrease memory and inference cost.

A neural architecture that uses attention to model relationships across sequences in parallel.

The model error value computed during training and optimized downward over time.

A transformer mechanism that runs several attention operations in parallel to capture different types of relationships.