Sol Video Inference Engine delivers more than 2x end-to-end speedup on video diffusion models while keeping VBench quality near-lossless - and it does it by treating acceleration as an instance-specific optimization problem solved by autonomous agents.
Why One-Size-Fits-All Acceleration Fails
Modern video diffusion models keep growing: 64B Cosmos3-Super, 22B LTX-2.3, 2B SANA-Video. Bigger models produce better video, but inference cost scales with them. The usual approach - pick one acceleration recipe and apply it everywhere - hits a wall because the optimal strategy changes with the model architecture, the hardware memory hierarchy, and the serving configuration (spatial/temporal resolution, video duration).
A technique that works for a 2B model on an H100 with short clips may cripple quality on a 64B model with long outputs. Manual tuning across this space is prohibitively expensive. The Sol team quantifies the tuning space as combinatorial across model, hardware, and inference settings.
Five Techniques, One Agent Stack
Sol organizes five broadly applicable acceleration techniques into an agent-driven stack: cache, sparse attention, token pruning, quantization, and kernel fusion. Each technique gets a parallel skill agent that optimizes its implementation for the concrete deployment target. An agent integrator then composes the individually tuned techniques into a global acceleration stack.
A human validator steps in only to provide quality feedback on the generated video, not to pick knobs. The framework is training-free - no fine-tuning or distillation required. This makes it practical for teams serving multiple model sizes across heterogeneous hardware.
Real Results on Three Model Sizes
The paper benchmarks the full stack on three very different models: the massive 64B Cosmos3-Super, the mid-size 22B LTX-2.3, and the compact 2B SANA-Video. With minimal human effort - just the validator's quality check - the stack achieves more than 2x end-to-end acceleration on all three, while VBench quality metrics remain near-lossless.
No single technique gets you that speedup alone. The composibility matters: quantization might deliver 1.3x, sparse attention another 1.4x, and caching another 1.5x, but the agent integration handles the interactions that manual tuning would miss. The result is a speedup that compounds without a quality cliff.
Expect this agent-native approach to become the default for deploying large diffusion models where optimal acceleration is a moving target across hardware generations and model releases.
Source: Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation
Domain: arxiv.org
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