Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation

2026-06-21

AI Summary

Video diffusion models suffer from high inference costs, and existing acceleration methods often fail to generalize across diverse model architectures, hardware platforms, and deployment configurations. To address this challenge, this work introduces a novel training-free, native acceleration framework grounded in multi-agent collaboration. The approach employs parallel skill agents that jointly optimize techniques including caching, sparse attention, token pruning, quantization, and operator fusion, while an orchestrating agent dynamically assembles a global acceleration stack tailored to specific deployment targets. Evaluated on three video diffusion models spanning different scales and architectures, the proposed method achieves over 2× end-to-end speedup while preserving near-lossless generation quality as measured by VBench metrics.

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.

Problem

• video diffusion models
• inference acceleration
• instance-specific optimization
• performance engineering
• hardware-model co-design

Innovation

• agent-native acceleration
• video diffusion models
• instance-specific optimization
• full-stack inference engine
• training-free acceleration