ltx-2.3-22b-dev-nvfp4.safetensors
LTX 2.3 Dev NVFP4 (Official)
NVFP4 quantized dev model. Native nvfp4 matmul on Blackwell GPUs (RTX 50xx). 21.7GB.
Download ltx-2.3-22b-dev-nvfp4.safetensors
Direct HuggingFace download. 21.7 GB · Free.
No 16GB GPU? Try ltx-2.3-22b-dev-nvfp4.safetensors online — free generation included
Skip the 21.7 GB download and ComfyUI setup. Generate a 6-second video using this exact model in your browser, ~30 seconds.
Will this run on my GPU?
Minimum: 16GB VRAM. Headroom up to: 24GB.
⚠ FP8 scaled matmul requires RTX 40-series or newer (Ada Lovelace architecture). RTX 30xx cannot run this format — use the MXFP8 block-32 or BF16 variant instead.
Recommendation: Best size/quality tradeoff on RTX 50xx. On older GPUs it falls back to slow paths — prefer FP8 there.
How to use ltx-2.3-22b-dev-nvfp4.safetensors
- Download the file from HuggingFace.
- Place it in ComfyUI/models/checkpoints/ inside your ComfyUI directory.
- Restart ComfyUI (or refresh the model list from the menu).
- Load a compatible workflow — see below.
Don't want to run this locally? Try ltx-2.3-22b-dev-nvfp4.safetensors online with a free generation — no GPU, no install, ~30 seconds per clip.
Common issues
ComfyUI doesn't see the file after I downloaded it▼
Make sure the file is in ComfyUI/models/checkpoints/ (not a subfolder). Restart ComfyUI fully — the menu refresh sometimes misses new files. Filename must match exactly: ltx-2.3-22b-dev-nvfp4.safetensors.
I get a CUDA error mentioning fp8 / scaled / matmul▼
FP8 scaled matmuls require an RTX 40-series GPU or newer (Ada Lovelace architecture). RTX 30-series and older cannot run FP8 weights at native precision. Use the BF16 variant instead, or the MXFP8 block-32 alternative.
CUDA out of memory error when loading the model▼
ltx-2.3-22b-dev-nvfp4.safetensors needs ~16GB VRAM minimum. If you're hitting OOM: • Enable Sequential Offloading in ComfyUI settings • Lower the resolution (768×512 instead of 1280×704) — both dimensions must be divisible by 32 • Reduce frame count (65 frames instead of 161) — must be 8n+1 • Use a smaller variant — see Related models below.
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