VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion

Researchers introduce VideoMLA, a novel approach that reduces KV cache memory requirements in video diffusion models by 92.7% through Multi-Head Latent Attention, enabling longer video generation with improved efficiency. The method challenges conventional assumptions about low-rank approximations in video models and demonstrates comparable quality to existing methods while improving throughput by 23%.

VideoMLA addresses a critical bottleneck in scaling autoregressive video diffusion models: the key-value cache that grows linearly with sequence length and computational demand. By implementing shared low-rank latent representations across attention heads rather than maintaining per-head caches, the research achieves substantial memory compression while maintaining output quality. This represents meaningful progress toward minute-scale video generation, a capability with significant implications for content creation, media production, and synthetic media applications.

The research's deeper contribution lies in its theoretical insights challenging existing assumptions in machine learning. Traditional low-rank approximation theory (spectral methods) would predict poor performance with this compression level, yet VideoMLA performs effectively. The authors demonstrate that architectural bottlenecks, not inherent spectral properties of pretrained models, determine effective rank in video attention. This finding suggests that neural networks adapt their representational structure to available capacity rather than relying on inherent sparsity, with implications extending beyond video diffusion to broader deep learning optimization.

The practical impact centers on computational efficiency and accessibility. The 23% throughput improvement on advanced hardware like NVIDIA's B200 translates to faster inference and reduced computational costs. For developers building video generation systems, this efficiency gain enables deployment on more modest hardware or supports longer content generation within existing resource constraints. The work demonstrates that architectural innovation remains crucial for scaling generative AI systems, particularly for memory-intensive tasks like video synthesis.

• →VideoMLA reduces KV cache memory by 92.7% through shared low-rank latent representations across attention heads
• →The method maintains quality at long-horizon video generation while achieving 1.23x throughput improvement on B200 hardware
• →Research challenges the spectral approximation theory underlying most low-rank compression, revealing architectural bottlenecks determine effective rank
• →Architectural innovations in attention mechanisms remain critical for scaling resource-intensive generative models
• →Efficiency gains enable longer video synthesis and broader hardware accessibility for video diffusion applications