context parallelism

At 1M tokens a 70B GQA model needs 320 GiB of KV per request and spends over 90% of prefill FLOPs in attention. The answer lays out a plan that fits on eight H100s, then shows which single change to the model breaks it and what replaces it.

Tensor parallelism across nodes would all-reduce every activation over the network. Splitting the sequence instead sends only the KV, which for a GQA model is small. The answer sizes that traffic, finds when it hides behind attention, and fixes the causal-mask imbalance that naive splitting creates.