A shared low‑rank cache slashes the memory footprint of autoregressive video diffusion by more than nine‑tenths while still permitting arbitrarily long rollouts.
Before these contributions, streaming video diffusion relied on a per‑head key‑value cache that grows linearly with the temporal window, forcing practitioners to cap video length or provision prohibitively large GPUs.
VideoMLA reduces per‑token KV cache memory by 92.7 % while preserving compatibility with standard chunk‑causal generation. The paper shows that this compression does not hurt visual fidelity; on VBench the method matches short‑horizon baselines and even secures the best long‑horizon score, while Table 3 reports the highest throughput and lowest latency among chunk‑wise autoregressive models, translating to a 1.23× speedup on a single B200 [1].
Echo‑Infinity achieves state‑of‑the‑art performance and, to our knowledge, demonstrates promising 24‑hour (>1.3 M frames) real‑time rollouts for the first time, suggesting a practical path toward infinite video generation. In practice the system runs at 18.5 FPS on a single NVIDIA H100 and incurs only a 10.6 % throughput overhead compared with a memory‑free baseline, proving that constant‑cost, evolving memory can sustain day‑scale generation without exploding resource use [2].
These results leave open several questions. VideoMLA’s latent dimension must be chosen manually, and although the bottleneck rank appears sufficient for the evaluated datasets, it is unclear how the approach scales to higher‑resolution or multi‑modal streams. Echo‑Infinity’s learnable memory, while effective up to a million frames, has not been stress‑tested on content that requires very long‑range narrative coherence, and the unified RoPE recipe may still encounter extrapolation limits on unseen motion dynamics.
If the combined system lives up to the reported numbers, developers can abandon the practice of over‑provisioning GPU memory for long video generation. Benchmarks that previously capped at a few seconds should be rerun with the minute‑scale configs shipped in the VideoMLA and Echo‑Infinity repositories, and production pipelines can target hour‑ or day‑scale output on a single H100 without redesigning the hardware stack.
