I am not an expert on LLMs, but what's preventing you from treating the context as 'virtual memory', and using the attention matrix to 'blank out' tokens which have very low weights and will not contribute much to the input? I imagine most tokens are like this, and you can skip computation on 95% of an 1M (or practically infinite) context.