Adaptive compute primitives now shave 30–50% off inference latency across video diffusion, large‑scale language flow models, and long‑context transformers. While ALLOT operates without any additional training, the other primitives rely on models that have been pre‑trained (Lafa’s flow model and MemT‑1.0’s memory‑native architecture) and achieve speedups through inference‑time techniques.

Historically, dense attention has been the dominant bottleneck in generative video pipelines; “On a single GPU, Wan2.1-14B takes roughly one hour to generate a 720p, 81‑frame video, and attention accounts for more than half of the end-to-end time” [1] .…