Introducing RunAsh Model

    Real-time / Live Streaming Video Generation vLLM

    RunAsh real-time/live streaming vLLM is optimized for efficiently fine-tuning Stable Video Diffusion SVD-XT to generate 25-30 frame videos with low latency and strong temporal consistency for live workflows.

    Real-time First
    Tuned inference profiles target stable, live-response generation loops for creator and commerce streams.
    SVD-XT Fine-tuning
    Efficient LoRA-style adaptation for 25-30 frame clip generation without full-model retraining.
    Model Access
    Includes paper, implementation notes, and download package for integration into streaming stacks.
    Resources
    Read technical details or download the RunAsh real-time model package.
    Read arXiv Paper Download Model Package
    Platform & Research Links
    Explore supporting platforms and the RunAsh research ecosystem.
    Hugging Face Kaggle Google Colab RunAsh RunAsh AI Research Lab
    Explore RunAsh LLM
    Need text-first model fine-tuning? Check the RunAsh LLM track.
    Open RunAsh LLM Page

    Current 2026 Real-time Model Contenders

    Snapshot of leading real-time video-generation model families and their common dataset directions.

    Google Veo
    High-fidelity text-to-video and cinematic control

    Dataset trend: Large-scale internal multimodal video corpus

    View arXiv paper
    Wan 2.1
    Open video generation for controllable motion/composition

    Dataset trend: Curated open text-video pairs with motion filtering

    View arXiv paper
    Self-Forcing
    Stable autoregressive long-horizon video generation

    Dataset trend: Self-forcing synthetic + real clip curriculum

    View arXiv paper
    Krea Real-Time
    Interactive, low-latency creative generation

    Dataset trend: Prompt-aligned short-form creative video sets

    View arXiv paper
    Runway (Gen family)
    Production-grade creative tooling and editing

    Dataset trend: Large-scale mixed licensed + synthetic video corpus

    View arXiv paper