How does distributed attention computation affect ChatGPT scalability in long-context models?

Distributed attention splits attention computation across multiple GPUs to handle long sequences efficiently.

Attention computation in transformers scales quadratically with sequence length, making long-context processing extremely expensive. To address this, distributed attention techniques partition sequence tokens across multiple GPUs or nodes.

Each device computes attention for a subset of tokens and exchanges intermediate results via high-speed communication layers. Techniques like ring attention or blockwise attention enable scaling to very long sequences.

This architecture introduces communication overhead but allows models to process contexts that exceed single-GPU memory limits.