Model × hardware × configuration aware acceleration
The framework optimizes each concrete deployment instance rather than assuming a single transferable acceleration recipe.
Agent-Native Full-Stack Acceleration
Sol Video Inference Engine
Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation
A training-free acceleration framework that composes cache, sparse attention, token pruning, quantization, and kernel fusion into deployment-ready stacks for video diffusion inference.
Cosmos3-Super2.27×LTX-2.32.38×SANA-Video2.77×
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Key Features
Acceleration that composes across the full stack
Cache, sparse attention, token pruning, quantization, and kernel fusion
Sol Video Inference Engine organizes five broadly applicable acceleration techniques into a deployment-ready full-stack framework.
Parallel skill agents with global integration
Per-technique local tuning provides strong starting points that are later composed through a global integrator and validation loop.
2×–3× end-to-end speedup
The full framework substantially reduces latency across Cosmos3-Super, LTX-2.3, and SANA-Video while preserving perceptual quality.
Local-to-Global Workflow
From local technique tuning to a validated global stack
Test technique applicability
Parallel skill agents probe each bounded technique space - cache, sparse attention, token pruning, quantization, and kernel fusion.
Compose candidates
An agent integrator combines the selected local techniques into a candidate deployment stack.
Validate quality
A human validator reviews efficiency-quality feedback and refines the composition.
Deploy the stack
A deployment-ready, instance-specific stack is produced for the target model, hardware, and serving configuration.
Abstract
Sol Video Inference Engine is a training-free, agent-native acceleration framework for video diffusion inference that composes cache, sparse attention, token pruning, quantization, and kernel fusion into deployment-ready stacks tailored to each model, hardware target, and serving configuration. Across Cosmos3-Super, LTX-2.3, and SANA-Video, the framework delivers more than 2× end-to-end speedup while maintaining near-lossless visual quality.
Efficiency at a Glance
More than 2× end-to-end speedup, near-lossless quality
Bounded design spaces
Sol Video Inference Engine exposes each technique as a bounded design space. Parallel skill agents test technique applicability, an agent integrator composes selected candidates into a deployment stack, and a human validator provides efficiency-quality feedback.
Cosmos3-Super2.27×LTX-2.32.38×SANA-Video2.77×
Demos
Side-by-side output, up to 2.77× faster
Each clip pairs a 1× baseline with the Sol Engine accelerated run.
SGLANG · 1× baselineSOL-ENGINE · 2.27× accelerated
BibTeX
Cite the Sol Video Inference Engine
@misc{li2026solvideoinferenceengine,
title={Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation},
author={Yitong Li and Junsong Chen and Haopeng Li and Haozhe Liu and Jincheng Yu and Ligeng Zhu and Ping Luo and Song Han and Enze Xie},
year={2026},
eprint={2606.23743},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.23743}
}FAQ
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