# LTX-Video by LTX.io: Open-Weight Model Source: https://savrn.com/models/ltx-video Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve LTX-Video (1.9B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 3.8 GB | 4.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 1.9 GB | 2.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 1.0 GB | 1.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [LTX-Video on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/ltx-video/gpus) ## SAVRN's Notes on LTX-Video Four and a half gigabytes is the whole hardware story for LTX-Video. The 1.9B-parameter image-to-video model carries 3.8 GB of 16-bit weights and needs 4.6 GB of memory, dropping to 2.3 GB at 8-bit and 1.2 GB at 4-bit. On the cheapest setup the SAVRN Index prices, one 192 GB MI300X at $1.85 an hour, that is under three percent of the card. Nobody sizes a rack around this; the decision is what else shares the GPU. The license is listed as other, and the publisher's card names a separate license for each 2B version from 0.9 through 0.9.6-dev, so the terms depend on the checkpoint you pull. The repository holds 63 files and 254 GB while the model you serve is under 4 GB: pick the version, read its license, fetch only that one. No Index host prices it, so the card hour is your cost reference. SAVRN Research, 2026-09-18 ## Model Card This model card focuses on the model associated with the LTX-Video model, codebase available here. LTX-Video is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 30 FPS videos at a 1216×704 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. You can use the model for purposes under the license: - 2B version 0.9: license - 2B version 0.9.1 license - 2B version 0.9.5 license - 2B version 0.9.6-dev license - 2B version 0.9.6-distilled license - 13B version 0.9.7-dev license - 13B version 0.9.7-dev-fp8 license… Excerpt from the card by LTX.io, licensed other. ## Identity and Version Repository Lightricks/LTX-Video Publisher LTX.io Task Image to video Modality Other Library diffusers Parameters 1.9B parameters Languages en Revision 8984fa25007f376c1a299016d0957a37a2f797bb First published 2024-10-31 Last updated 2025-07-16 ## Files and Weights 63 files, 254.1 GB in total. The weights are 27 files totalling 253.8 GB in safetensors. Weights27 files · 253.8 GB Configuration9 files · 99.7 KB Tokenizer2 files · 812.3 KB Documentation4 files · 69.2 KB Other20 files · 297.0 MB Repository1 file · 3.4 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | ltx-video-2b-v0.9.1.safetensors | Weights | 5.7 GB | a23200896c5e | | ltx-video-2b-v0.9.5.safetensors | Weights | 6.3 GB | 720d15c9f19f | | ltx-video-2b-v0.9.safetensors | Weights | 9.4 GB | 45369c2ae94e | | ltxv-13b-0.9.7-dev-fp8.safetensors | Weights | 15.7 GB | b281bbb53b76 | | ltxv-13b-0.9.7-dev.safetensors | Weights | 28.6 GB | 56b39a874ef9 | | ltxv-13b-0.9.7-distilled-fp8.safetensors | Weights | 15.7 GB | d79f419b7203 | | ltxv-13b-0.9.7-distilled-lora128.safetensors | Weights | 1.3 GB | c6004b2d7b29 | | ltxv-13b-0.9.7-distilled.safetensors | Weights | 28.6 GB | ae3b07c28f19 | | ltxv-13b-0.9.8-dev-fp8.safetensors | Weights | 15.7 GB | b281bbb53b76 | | ltxv-13b-0.9.8-dev.safetensors | Weights | 28.6 GB | 56b39a874ef9 | | ltxv-13b-0.9.8-distilled-fp8.safetensors | Weights | 15.7 GB | 111a3d07baa1 | | ltxv-13b-0.9.8-distilled.safetensors | Weights | 28.6 GB | 2c5f814744f0 | | ltxv-2b-0.9.6-dev-04-25.safetensors | Weights | 6.3 GB | e6fb3fe995e2 | | ltxv-2b-0.9.6-distilled-04-25.safetensors | Weights | 6.3 GB | 94891bd4bd08 | | ltxv-2b-0.9.8-distilled-fp8.safetensors | Weights | 4.5 GB | d6d8fa8ed3a9 | | ltxv-2b-0.9.8-distilled.safetensors | Weights | 6.3 GB | 76aa8c4786af | | ltxv-spatial-upscaler-0.9.7.safetensors | Weights | 505.0 MB | 5b076031c6f8 | | ltxv-spatial-upscaler-0.9.8.safetensors | Weights | 505.0 MB | 5b076031c6f8 | | ltxv-temporal-upscaler-0.9.7.safetensors | Weights | 523.9 MB | 92ee7c34a060 | | ltxv-temporal-upscaler-0.9.8.safetensors | Weights | 523.9 MB | 92ee7c34a060 | | text_encoder/model-00001-of-00004.safetensors | Weights | 5.0 GB | 7a68b2c8c080 | | text_encoder/model-00002-of-00004.safetensors | Weights | 5.0 GB | b8ed6556d750 | | text_encoder/model-00003-of-00004.safetensors | Weights | 4.9 GB | c831635f8304 | | text_encoder/model-00004-of-00004.safetensors | Weights | 4.2 GB | 02a5f2d69205 | | transformer/diffusion_pytorch_model-00001-of-00002.safetensors | Weights | 4.9 GB | 8acd3e0bda74 | | transformer/diffusion_pytorch_model-00002-of-00002.safetensors | Weights | 2.8 GB | 03b3c822c31e | | vae/diffusion_pytorch_model.safetensors | Weights | 1.7 GB | 265ca87cb5df | | model_index.json | Configuration | 412 B | — | | scheduler/scheduler_config.json | Configuration | 418 B | — | | text_encoder/config.json | Configuration | 781 B | — | | text_encoder/model.safetensors.index.json | Configuration | 19.9 KB | — | | tokenizer/added_tokens.json | Configuration | 2.6 KB | — | | tokenizer/special_tokens_map.json | Configuration | 2.5 KB | — | | transformer/config.json | Configuration | 500 B | — | | transformer/diffusion_pytorch_model.safetensors.index.json | Configuration | 72.1 KB | — | | vae/config.json | Configuration | 501 B | — | | README.md | Documentation | 21.1 KB | — | | ltx-video-2b-v0.9.1.license.txt | Documentation | 16.0 KB | — | | ltx-video-2b-v0.9.5.license.txt | Documentation | 16.1 KB | — | | ltx-video-2b-v0.9.license.txt | Documentation | 16.0 KB | — | | LTX-Video-Open-Weights-License-0.X.txt | Other | 14.5 KB | — | | media/ltx-video_example_00001.gif | Other | 8.0 MB | b679f14a09d2 | | media/ltx-video_example_00005.gif | Other | 5.7 MB | 87fdb9556c12 | | media/ltx-video_example_00006.gif | Other | 132 B | ac92edd9c2e7 | | media/ltx-video_example_00007.gif | Other | 132 B | 68d15278b9d7 | | media/ltx-video_example_00010.gif | Other | 6.2 MB | bcf1e084e936 | | media/ltx-video_example_00011.gif | Other | 132 B | 15b67e58e7bc | | media/ltx-video_example_00013.gif | Other | 9.0 MB | aa7eb790b43f | | media/ltx-video_example_00014.gif | Other | 132 B | ae7c3cdb08be | | media/ltx-video_example_00015.gif | Other | 132 B | af8e94d5b982 | | media/ltx-video_i2v_example_00001.gif | Other | 9.3 MB | 51e7f2ef92a9 | | media/ltx-video_i2v_example_00002.gif | Other | 20.4 MB | 0bd2c1314587 | | media/ltx-video_i2v_example_00003.gif | Other | 9.8 MB | bb62a7934088 | | media/ltx-video_i2v_example_00004.gif | Other | 15.7 MB | 151cb985a162 | | media/ltx-video_i2v_example_00005.gif | Other | 10.3 MB | 6280efa40b66 | | media/ltx-video_i2v_example_00006.gif | Other | 11.9 MB | da23e79524a6 | | media/ltx-video_i2v_example_00007.gif | Other | 11.8 MB | 9bcfe336e823 | | media/ltx-video_i2v_example_00008.gif | Other | 14.7 MB | 706368111eda | | media/ltx-video_i2v_example_00009.gif | Other | 16.8 MB | a9c13259e014 | | media/trailer.gif | Other | 147.3 MB | fe193ca2f5d6 | | .gitattributes | Repository | 3.4 KB | — | | tokenizer/spiece.model | Tokenizer | 791.7 KB | d60acb128cf7 | | tokenizer/tokenizer_config.json | Tokenizer | 20.6 KB | — | ## License and Download License other Access Open weights, no gate Download size 253.8 GB [Download from LTX.io](https://huggingface.co/Lightricks/LTX-Video) Released by LTX.io through its official repository on Hugging Face. ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 253.8 GB | | 16-bit | 3.8 GB | | 8-bit | 1.9 GB | | 4-bit | 1.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About LTX-Video ### How much GPU memory does LTX-Video need? About 4.6 GB at 16-bit and 1.2 GB at 4-bit: the weights (1.9B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run LTX-Video on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### What license is LTX-Video released under? other, as its publisher declares it. Read the license text before commercial use. ## Similar Models Model · Image to video ### [Wan-2.2](https://savrn.com/models/wan-2-2) [Comfy Org](https://savrn.com/model-publishers/comfy-org) Repackaged model files for ComfyUI. - https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers - https://huggingface.co/Wan-AI/Wan2.2-Animate-14B - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-Control-Camera - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-Control - https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control - https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-InP - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-InP - https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B - https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B - https://huggingface.co/Wan-AI/Wan2.2-S2V-14B - https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B - https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B… Open weights apache-2.0 diffusion-single-file [View model](https://savrn.com/models/wan-2-2) Model · Image to video ### [Wan_2.2_ComfyUI_Repackaged](https://savrn.com/models/wan-2-2-comfyui-repackaged) [Comfy Org](https://savrn.com/model-publishers/comfy-org) Repackaged model files for ComfyUI. - https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers - https://huggingface.co/Wan-AI/Wan2.2-Animate-14B - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-Control-Camera - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-Control - https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-Control - https://huggingface.co/alibaba-pai/Wan2.2-Fun-5B-InP - https://huggingface.co/alibaba-pai/Wan2.2-Fun-A14B-InP - https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B - https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B - https://huggingface.co/Wan-AI/Wan2.2-S2V-14B - https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B - https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B… Open weights apache-2.0 diffusion-single-file [View model](https://savrn.com/models/wan-2-2-comfyui-repackaged) Model · Image to video ### [LTX-2.5](https://savrn.com/models/ltx-2-5) [LTX.io](https://savrn.com/model-publishers/lightricks) LTX-2.5 is an open world model with open weights, built for local execution and fine-tuning. Its established use is generating synchronized, high-fidelity video and audio from text, image, and video inputs; applicability to emerging domains such as robotics and physical AI is developing. Full control and customization — self-host on your own infrastructure. No per-generation billing, no per-seat lock-in, no forced API dependency. Revenue is measured across the whole entity, including subsidiaries and affiliates under common control. The full, binding terms live in LICENSE. - Native multishot generation — generate connected scenes in a single pass: multiple shots that hold character… Access requested at publisher other diffusion-single-file [View model](https://savrn.com/models/ltx-2-5) Model · Image to video ### [LTX-2.5](https://savrn.com/models/ltx-2-5-2) [Comfyicu](https://savrn.com/model-publishers/comfyicu) LTX-2.5 is an open world model with open weights, built for local execution and fine-tuning. Its established use is generating synchronized, high-fidelity video and audio from text, image, and video inputs; applicability to emerging domains such as robotics and physical AI is developing. Full control and customization — self-host on your own infrastructure. No per-generation billing, no per-seat lock-in, no forced API dependency. Revenue is measured across the whole entity, including subsidiaries and affiliates under common control. The full, binding terms live in LICENSE. - Native multishot generation — generate connected scenes in a single pass: multiple shots that hold character… Open weights other diffusion-single-file [View model](https://savrn.com/models/ltx-2-5-2) Model · Image to video ### [MiniMax-H3-GGUF](https://savrn.com/models/minimax-h3-gguf) [Jay](https://savrn.com/model-publishers/abiray) This repository (Abiray/MiniMax-H3-GGUF) provides GGUF quantized versions and necessary component files for the MiniMax H3 model. MiniMax H3 is a general-purpose, omni-modal generative system that supports unified understanding of multimodal contexts composed of text, images, video, and audio. It can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. If you are looking for a smaller model with the same great quality that fits better on consumer-tier GPUs, please check out the MiniMax-H3-Pruned-GGUF repository. The pruned architecture is compressed down to 8.9 GB – 21.6 GB, bringing MiniMax H3 execution directly to consumer hardware. This… Open weights other [View model](https://savrn.com/models/minimax-h3-gguf) Model · Image to video ### [LTX-2.3](https://savrn.com/models/ltx-2-3) [LTX.io](https://savrn.com/model-publishers/lightricks) This model card focuses on the LTX-2.3 model, which is a significant update to the LTX-2 model with improved audio and visual quality as well as enhanced prompt adherence. LTX-2 was presented in the paper LTX-2: Efficient Joint Audio-Visual Foundation Model. If you want to dive in right to the code - it is available here. LTX-2.3 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution. LTX-2.3 is accessible right away via the API Playground. You can use the models - full, distilled, upscalers and any… Open weights other diffusers [View model](https://savrn.com/models/ltx-2-3) ## LTX.io [All models and datasets](https://savrn.com/model-publishers/lightricks) ## Versions - [8984fa25007f](https://savrn.com/models/ltx-video/versions/8984fa25007f) · current 2026-09-18 ## Explore More - [All image to video models](https://savrn.com/models/tasks/image-to-video) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/Lightricks/LTX-Video) - [How the hub is built](https://savrn.com/model-hub/methodology)