What is the Attention Complexity problem in Transformers and how do modern architectures solve it?

Standard Transformer attention has quadratic complexity with respect to sequence length, creating major scalability bottlenecks for long-context AI systems.

The self-attention mechanism is one of the most powerful innovations in deep learning, but it comes with a severe computational limitation.

In standard self-attention:

O(n²)

Where n is sequence length.

This creates major problems for:

Memory and compute explode rapidly.

Example:

Core bottlenecks: 1.…