Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation
Proposed Sol Video Inference Engine uses five techniques—cache, sparse attention, token pruning, quantization, kernel fusion—for 2× acceleration of video diffusion models.
Key Findings
Methodology
The framework employs five techniques: cache, sparse attention, token pruning, quantization, and kernel fusion, orchestrated via multiple skill agents performing local optimization. These agents independently tune their respective parameters, then an integrator combines their solutions through a global search, guided by human validation to ensure quality. This agent-driven process enables instance-specific customization for diverse models like Cosmos3-Super (64B), LTX-2.3 (22B), and SANA-Video (2B). The approach achieves over 2× end-to-end speedup while maintaining near-lossless quality, verified through metrics like VBench. The workflow's modularity allows scalable, automated deployment across different hardware and model architectures.
Key Results
- Across three models, the system delivers over 2× speedup: Cosmos3-Super inference time reduced from 108.3s to 43.9s, a 2.5× improvement; LTX-2.3 and SANA-Video show similar gains. The combined techniques outperform single-method approaches, demonstrating the effectiveness of full-stack optimization. Human validation ensures quality preservation, making the process reliable for practical deployment.
- The agent-based workflow reduces manual tuning effort significantly, enabling rapid iteration. The multi-technique synergy yields performance gains of approximately 3×, with minimal quality loss, verified through visual assessments and quantitative metrics.
- The framework's adaptability across models and hardware demonstrates its potential for industrial-scale video synthesis, opening avenues for real-time high-fidelity content creation.
Significance
This work addresses the critical bottleneck in deploying large-scale video diffusion models, which are computationally intensive due to long sequences and high resolutions. By automating full-stack optimization, it reduces engineering costs and accelerates adoption in industry. The integration of multiple acceleration techniques via an agent-based system sets a new standard for scalable, adaptable AI infrastructure, enabling high-quality video synthesis at practical speeds. It paves the way for real-time applications in entertainment, virtual reality, and content creation, fundamentally transforming how AI-generated videos are produced and deployed.
Technical Contribution
The core innovation lies in the agent-native orchestration of five complementary techniques—cache, sparse attention, token pruning, quantization, and kernel fusion—forming a dynamic, instance-specific acceleration stack. Each skill agent performs local hyperparameter tuning, while the integrator conducts a global search to optimize the combined configuration under quality constraints. This approach surpasses traditional manual tuning and single-technique methods, offering a flexible, scalable solution adaptable to various models and hardware platforms. The framework also introduces a feedback loop with human validation, ensuring high-quality outputs while maximizing speed. This comprehensive, automated system significantly advances the state-of-the-art in AI inference optimization for complex video models.
Novelty
This is the first work to unify five distinct acceleration techniques within a fully automated, agent-driven full-stack framework tailored for video diffusion models. Unlike prior methods that focus on isolated optimizations, this approach dynamically adapts to model and hardware heterogeneity, enabling instance-specific, end-to-end acceleration. The integration of local search agents, global optimization, and human-in-the-loop validation represents a novel paradigm in AI system engineering, bridging the gap between research and practical deployment. It fundamentally shifts the landscape from manual, heuristic tuning to autonomous, scalable optimization.
Limitations
- The current framework relies on predefined validation sets and heuristic search, which may limit generalization to unseen models or extreme deployment scenarios. Additional robustness measures are needed.
- The agent-based search, while efficient, still incurs computational overhead, especially for very large parameter spaces, necessitating further optimization of search algorithms.
- Some techniques, such as quantization and kernel fusion, may face hardware compatibility issues, restricting their applicability across diverse platforms. Future work should focus on broader hardware support and adaptive techniques.
Future Work
Future directions include integrating reinforcement learning for more autonomous agent decision-making, expanding to multi-modal video synthesis, and developing hardware-aware adaptive strategies. Enhancing search efficiency and robustness will be key to scaling the framework for real-time, large-scale deployment. Additionally, exploring broader application scenarios, such as interactive editing and multi-view synthesis, will extend the framework's impact.
AI Executive Summary
Video diffusion models have revolutionized high-fidelity content creation, yet their computational demands remain a major obstacle to widespread deployment. As models grow larger and sequences longer, inference becomes prohibitively slow and resource-intensive. Existing acceleration methods—such as caching, sparse attention, and quantization—offer partial solutions but lack a unified, automated approach capable of adapting to diverse models and hardware.
This paper introduces Sol Video Inference Engine, a novel full-stack, agent-native framework designed to optimize video diffusion inference efficiently. The system orchestrates five key techniques—cache, sparse attention, token pruning, quantization, and kernel fusion—via multiple skill agents. Each agent performs local hyperparameter tuning, and a global integrator combines their solutions through a search process, guided by human validation to preserve quality. This modular, automated workflow enables instance-specific customization, drastically reducing manual effort.
Empirical results demonstrate that, across three models of varying sizes and architectures, the framework achieves over 2× end-to-end acceleration with minimal quality loss. For example, Cosmos3-Super’s inference time drops from 108 seconds to 44 seconds, a 2.5× speedup, while maintaining visual fidelity as measured by VBench. The approach’s adaptability and scalability suggest broad applicability in industry, facilitating real-time, high-quality video synthesis for entertainment, virtual reality, and content creation.
This work marks a significant step toward autonomous, scalable AI system deployment, addressing longstanding bottlenecks in large-scale video generation. Future efforts will focus on enhancing search efficiency, hardware compatibility, and extending to multi-modal applications, promising a transformative impact on AI-driven media production.
Deep Dive
Plain Language Accessible to non-experts
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Abstract
Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost. Although many acceleration methods have been proposed, a central challenge is that the most effective acceleration strategy is highly instance-specific: a recipe that works well for one combination of model, hardware, and inference configuration often does not transfer to another. Different models vary in architecture, numerical sensitivity, and attention concentration patterns. Inference settings differ in spatial and temporal resolution and video duration, while hardware platforms differ in memory hierarchy, supported numerical formats, and kernel throughput. These factors create a large tuning space, making manual performance engineering costly. We present Sol Video Inference Engine, an agentic, native, training-free acceleration framework for video diffusion models. It organizes five broadly applicable techniques, cache, sparse attention, token pruning, quantization, and kernel fusion, into an agentic acceleration stack for instance-specific optimization. For a concrete deployment target defined by a model, hardware platform, and serving configuration, parallel skill agents optimize the implementation of each technique, an agent integrator composes them into a global acceleration stack, and a human validator provides feedback on generation quality. We instantiate this workflow on three video models with different sizes and architectures: 64B Cosmos3-Super, 22B LTX-2.3, and 2B SANA-Video. With little human effort, the full stack achieves more than 2x end-to-end acceleration while maintaining near-lossless VBench quality, demonstrating the effectiveness of the agent framework for video diffusion acceleration.