Run AI Video Locally: LTX-2, RTX, and ComfyUI in 2026

The open-source model LTX-2, developed by Lightricks in collaboration with NVIDIA, now generates up to 20 seconds of 4K video at 50 FPS on a single consumer GPU. No cloud. No subscription. No data leaving your machine.

At CES 2026, NVIDIA demonstrated LTX-2 running natively on the new RTX 50 Series, and the results left the audience speechless. This was not a research demo. It was production-ready local video generation, running at speeds that rival cloud services.

Let me walk you through what this means, what you need, and how to set it up.

Why Local AI Video Generation Matters

Before diving into the technical setup, let me explain why running AI video locally is not just a hobbyist preference. It solves real problems.

For studios handling client footage, privacy is not optional. For creators in regions with slow internet, cloud generation is impractical. And for anyone who generates hundreds of clips per month, the subscription math stops making sense very quickly.

What You Need: Hardware Requirements

Here is the honest breakdown. Local generation requires decent hardware, but probably not as extreme as you think.

GPU Performance Comparison

The performance gap between generations is dramatic. Here is what NVIDIA demonstrated at CES 2026:

The RTX 50 Series achieves its 3x speedup through native NVFP4 quantization, halving the precision of model weights without visible quality loss. This is the same trick that made LLMs practical on consumer hardware, now applied to video diffusion.

LTX-2: What Makes It Special

LTX-2 is not simply "another open-source model." It represents a fundamental shift in what is possible on consumer hardware.

How It Compares to Cloud Services

Let me be specific about what local generation can and cannot do compared to the big cloud platforms.

The trade-off is clear: cloud services offer more polished outputs with less setup, while local generation gives you control, privacy, and zero marginal cost per generation. For a deeper look at how these cloud platforms compare, see our Sora 2 vs Runway vs Veo 3 comparison.

Setting Up LTX-2 with ComfyUI: Step by Step

Here is the practical walkthrough. I am assuming you have an NVIDIA GPU with at least 8 GB of VRAM and a recent driver installed.

Step 1: Install ComfyUI

ComfyUI is a node-based visual interface for diffusion models. Think of it as the Unreal Engine Blueprint system, but for AI generation workflows.

# Clone ComfyUI
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
 
# Create a virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate   # Windows
 
# Install dependencies
pip install -r requirements.txt
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124

Step 2: Download LTX-2 Model

# Navigate to the models directory
cd models/checkpoints
 
# Download LTX-2 (approximately 15 GB)
wget https://huggingface.co/Lightricks/LTX-Video-2/resolve/main/ltx-video-2-0.safetensors
 
# Download the VAE
cd ../vae
wget https://huggingface.co/Lightricks/LTX-Video-2/resolve/main/ltx-video-2-vae.safetensors

Step 3: Install the LTX-2 ComfyUI Extension

cd ../../custom_nodes
git clone https://github.com/Lightricks/ComfyUI-LTXVideo.git
cd ComfyUI-LTXVideo
pip install -r requirements.txt

Step 4: Launch and Generate

# Return to ComfyUI root
cd ../..
python main.py --listen 127.0.0.1 --port 8188

Open http://127.0.0.1:8188 in your browser. Load the LTX-2 workflow from the extension's example folder, type your prompt, and hit "Queue Prompt."

Optimizing Your Setup

After the basic setup works, here are the tuning tricks that make a real difference.

VRAM Management

The single biggest bottleneck for local generation is VRAM. Here is how to maximize what you have:

• ✓Enable model offloading (moves unused layers to system RAM)
• ✓Use NVFP8/NVFP4 quantization for your GPU generation
• ✓Close browser tabs and other GPU-hungry applications
• ✓Set Windows to "Hardware-accelerated GPU scheduling" in display settings
• ✓Consider a Linux dual-boot (5-10% better GPU utilization vs Windows)

Batch Processing Workflow

For creators who need to generate many clips, ComfyUI supports batch queuing. Set up your prompts in a JSON file, connect them to a batch loader node, and let your GPU work overnight. I have generated 200+ clips in a single overnight session on an RTX 4090.

LoRA Training for Custom Styles

This is where local generation truly shines. You can train a LoRA adaptor on 50-100 frames of reference footage in about 30 minutes on an RTX 4090. The result is a lightweight file (typically 100-300 MB) that biases the model toward your specific visual style, character appearances, or environment aesthetics.

# Example LoRA training command
python train_lora.py \
  --model ltx-video-2-0.safetensors \
  --data ./my_reference_frames/ \
  --output ./loras/my_style.safetensors \
  --steps 1000 \
  --lr 1e-4 \
  --rank 32

The Cost Calculation

Let me put concrete numbers to this. Assume you are a freelance video creator generating 100 five-second clips per month.

An RTX 4090 costs around $1,600 at MSRP, though current market prices hover closer to $2,500-2,800 due to discontinued production. At 100 clips per month using cloud services, even at market price the GPU pays for itself within 18-24 months, and then you are generating for the cost of electricity.

What Is Coming Next

The trajectory of local AI video generation is accelerating. Three developments to watch:

The Bottom Line

Cloud AI video services are excellent products. Sora, Veo, Runway, they all deliver impressive results with minimal setup. But they come with trade-offs that many creators are starting to find unacceptable: recurring costs, privacy concerns, internet dependency, and limited customization.

LTX-2 with ComfyUI on an NVIDIA RTX GPU is not a compromise. It is a different paradigm. One where you own the entire pipeline, from model weights to final output. The setup takes an afternoon. The learning curve is real but manageable. And the results, especially at 4K with the latest optimizations, are genuinely competitive with cloud alternatives.

If you have an RTX GPU gathering dust between gaming sessions, give it a second job. You might be surprised at what it can do.

Sources

• Lightricks: The official Lightricks repository documents local video generation on an RTX 4060 with 8 GB… (Lightricks)