Video models · Lightricks

LTX-2.5 (22B, video + audio)

Name: LTX-2.5 (22B, video + audio)
Author: Lightricks

Backbone: 21.0B
Native output: 1280×704, 121 frames
Default steps: 8
License: ltx-2.x-community
Released: 2026-08

How to run it

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

huggingface.co/unsloth/LTX-2.5-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Lightricks, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Lightricks/LTX-2.5 (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

Reference code, inference scripts and docs from Lightricks. The weights themselves download from Hugging Face.

github.com/Lightricks/LTX-2 (opens in a new tab)

git clone https://github.com/Lightricks/LTX-2

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

Apps that run LTX-2.5 (22B, video + audio)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Draw Things

Native Mac app with its own fast Metal engine and quantized downloads.

Also good:

Windows PC, NVIDIA GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

Linux, NVIDIA or AMD GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

App	Type	Opens it as	Systems	Engine	Start
ComfyUI	Desktop app	Original or GGUF	macOSWindowsLinux	PyTorch, GGUF via ComfyUI-GGUF	Get ComfyUI
Draw Things	Desktop app	Built-in support	macOSiOS	Draw Things	Open in Draw Things
SwarmUI	Desktop app	Original or GGUF	macOSWindowsLinux	ComfyUI	Get SwarmUI
SD.Next	Desktop app	Original or GGUF	macOSWindowsLinux	PyTorch	Get SD.Next
Stability Matrix	Desktop app	Original or GGUF	macOSWindowsLinux	installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus	Get Stability Matrix
WanGP	Desktop app	Built-in support	WindowsLinux	PyTorch	Get WanGP

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Peak GPU memory at the native output size. Offloading moves the text encoder, or the whole pipeline, to system RAM between steps.

Precision	Everything on GPU	Text encoder offloaded	Sequential offload	Download
`FP16`	82.5 GB	56.2 GB	27.1 GB	Original weights (opens in a new tab)
`FP8`	48.3 GB	35.2 GB	19.7 GB	Original weights (opens in a new tab)
`Q8 GGUF`	49.8 GB	36.7 GB	20.3 GB	GGUF (opens in a new tab)
`Q4 GGUF`	39.9 GB	26.8 GB	16.8 GB	GGUF (opens in a new tab)

Components

Backbone: 21B
VAE: 0.91B
Gemma 4 12B: 13.12B

Best hardware for LTX-2.5 (22B, video + audio)

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7900 XT (launch price $899) at Q4 GGUF.

Quality	Hardware	Quant	Memory	Speed	Runs
88Excellent quality (A+) · 88 of 100: how capable the model is at FP16, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GB10 (128 GB)	`FP16`	82.5 GB / 115 GB	3.2 min	Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at FP16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)	`FP16`	82.5 GB / 102 GB	3.3 min	Runs great
87Excellent quality (A+) · 87 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`Q8 GGUF`	49.8 GB / 96.0 GB	77 s	Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at FP8, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	AMD Ryzen AI Max+ 395 (128 GB)	`FP8`	48.3 GB / 96.0 GB	2.6 min	Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at FP16, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	Apple M2 Ultra (192 GB)	`FP16`	82.5 GB / 144 GB	6.6 min	Runs well
81Great quality (A) · 81 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5090	`Q4 GGUF`	26.8 GB / 32.0 GB	1.6 min	Runs greatencoder in RAM
81Great quality (A) · 81 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Max (64 GB)	`Q4 GGUF`	39.9 GB / 48.0 GB	10.7 min	Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at FP16, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (256 GB)	`FP16`	82.5 GB / 192 GB	6.4 min	Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Max (128 GB)	`FP8`	48.3 GB / 96.0 GB	8.0 min	Runs well
87Excellent quality (A+) · 87 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`Q8 GGUF`	20.3 GB / 24.0 GB	2.5 min	Runs greatCPU offload

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.