HappyHorseHappyHorse Model
Tutorials2 min readApril 2026

How to Run Open Source AI Video Models Locally (2026 Guide)

While HappyHorse-1.0 has no downloadable weights yet, several competitive open-source video models can be run locally today. This guide covers hardware requirements, setup, and practical tips.

Hardware Requirements

Minimum (720p, short clips)

  • GPU: RTX 4070 or equivalent (12GB VRAM)
  • RAM: 32GB
  • Storage: 50GB free (model weights + temp files)
  • Models that work: LTX Video 2.3, quantized WAN 2.1

Recommended (1080p, longer clips)

  • GPU: RTX 4090 or A100 (24-80GB VRAM)
  • RAM: 64GB
  • Storage: 100GB+
  • Models that work: WAN 2.6, Hunyuan Video, full-precision models

When HappyHorse Drops

Based on the claimed 15B parameters, HappyHorse-1.0 will likely require:

  • Full precision: 60GB+ VRAM (A100/H100)
  • FP8 quantized: 20-30GB VRAM (RTX 4090 possible)
  • INT4 quantized: 10-15GB VRAM (consumer GPU possible, quality TBD)

Setting Up Your First Local Model

Option 1: ComfyUI (Recommended for beginners)

ComfyUI provides a visual workflow interface that makes it easy to experiment with different models and settings.

  1. Clone ComfyUI and install dependencies
  2. Download model weights to models/ directory
  3. Load a video generation workflow
  4. Adjust resolution, steps, and prompt
  5. Generate

The LTX Video 2.3 community has excellent ComfyUI nodes with LoRA training support for 16GB VRAM cards.

Option 2: Direct Inference Scripts

For production pipelines, use the model's native inference code:

  1. Set up a Python virtual environment
  2. Install model-specific dependencies
  3. Download weights from HuggingFace
  4. Run inference script with your prompt

Option 3: Docker (Best for reproducibility)

Most open-source models provide Docker images or Dockerfiles. This ensures dependency isolation and makes deployment reproducible across machines.

Model-Specific Guides

WAN 2.6 (Alibaba)

  • Weights: HuggingFace (Apache 2.0)
  • VRAM: 24GB+ recommended
  • Strengths: Strong T2V quality, active community, good documentation
  • ComfyUI: Full support with custom nodes

LTX Video 2.3 (Lightricks)

  • Weights: HuggingFace (Apache 2.0)
  • VRAM: 12GB minimum
  • Strengths: Fastest inference on consumer GPUs, LoRA training, active community
  • ComfyUI: Excellent node support, V2V workflows

Hunyuan Video (Tencent)

  • Weights: HuggingFace (Tencent License)
  • VRAM: 40GB+ recommended
  • Strengths: Good quality, Chinese language support
  • Limitation: License restricts some commercial uses

Optimization Tips

  1. Use quantized models when possible — FP8 offers near-identical quality at half the VRAM
  2. Enable attention slicing to reduce peak memory usage
  3. Lower resolution first, upscale later with a dedicated super-resolution model
  4. Batch by resolution — don't mix 720p and 1080p jobs
  5. Monitor VRAM with nvidia-smi to avoid OOM crashes

Preparing for HappyHorse-1.0

When weights drop, expect:

  • Initial release will likely be full-precision (60GB+)
  • Community quantizations within 24-48 hours
  • ComfyUI nodes within a week
  • LoRA training support within 2-3 weeks

Set up your inference environment now with WAN 2.6 or LTX 2.3 so you're ready to swap models when HappyHorse becomes available.