APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
arXiv:2601.21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex videos. To overcome these issues, we propose APB-V, a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs. By distri
The increasing complexity and length of video data are pushing the limits of current LMMs, driving innovation in more efficient processing techniques.
This development addresses a critical bottleneck in LMM scalability, enabling more sophisticated and longer-duration video understanding essential for advanced AI applications.
The ability to efficiently process long videos across multiple GPUs will expand the applications of LMMs into fields previously constrained by computational limits.
- · AI compute providers
- · Large Multimodal Model developers
- · Video analytics companies
- · Cloud service providers
- · Single-GPU inference solutions
- · Inefficient video processing algorithms
Significantly faster and more scalable long-video inference becomes possible for LMMs.
New AI applications emerge that rely on real-time, long-duration video understanding across industries like surveillance, autonomous vehicles, and media.
The demand for high-bandwidth, multi-GPU compute infrastructure could accelerate due to broadened LMM capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL