How does context window extension impact memory, latency, and inference architecture in ChatGPT?

Extending context windows increases memory and compute costs, requiring architectural optimizations like sparse attention and KV compression.

Increasing context window size in ChatGPT-like models significantly impacts memory usage because KV-cache grows linearly with tokens, while attention computation grows quadratically. This leads to higher GPU memory consumption and slower inference.

To support larger contexts, systems use techniques such as KV compression, sliding window attention, and hierarchical memory management. Some architectures also use chunking and retrieval augmentation instead of fully expanding context.

The tradeoff is between long-range reasoning capability and computational efficiency.