Flux Attention halves inference cost on long contexts

Dynamic sparse routing now delivers two‑ to three‑fold speedups on long‑context inference while leaving reasoning quality virtually untouched. The trick is that each transformer layer decides on the fly whether to attend densely or sparsely, reducing the blanket‑over‑all quadratic cost associated with standard attention in large language models. The result is a practical, drop‑in acceleration that works on the chat‑style workloads that dominate production today. Standard self‑attention scales as