Dedicated model · Available as managed deployment

LTX-2.5 video generation in Europe: managed deployment

Lightricks' LTX-2.5 is an open-weight image-to-video model built for fast, controllable clip generation, with a Diffusers build for standard pipelines. Available as a managed deployment: validated on AxForge hardware and deployed on a DGX Spark for your traffic only, as an OpenAI-compatible endpoint on hardware only you use, hosted in the EU.

Available as managed deployment eu-es-1 · Málaga Image or text → video ltx-2-5.axforge.ai
Request deployment Qwen3.8 27B, on the API now Hour, week, month or year. Hardware from €0.85/hour; managed service quoted per deployment.

Why LTX-2.5

What you get

Fast image-to-videoLTX is the family known for speed: usable clips in a fraction of the time of larger video models.
Standard pipelineA Diffusers build means it drops into existing tooling (ComfyUI, custom pipelines) on your own machine.
Licence reviewedLTX-2.5 ships under Lightricks' own licence; AxForge reviews it with you and deploys under it where it applies.

Specifications

The model

ModelLTX-2.5 by Lightricks
ModalitiesImage or text → video
Sizes19.0B
Released2026-07
Popularity1.1M downloads / month on HuggingFace
LicenceCommercial licence needed (the model's own licence); AxForge handles the licence where one is required
AvailabilityAvailable as managed deployment, operated by AxForge on hardware reserved for you
HardwareNVIDIA DGX Spark (GB10, 128 GB unified memory), owned and operated by AxForge
RegionMálaga, Spain (eu-es-1)
PricingHardware from €0.85/hour, rental term: Hour, week, month or year. Managed service quoted per deployment. Request deployment and we confirm both in writing.

We publish only numbers we measure ourselves, and we haven't benchmarked this model on our nodes yet. For quality benchmarks, see the model card in the catalogue and the official card on HuggingFace.

Deployment

What "managed deployment" means

SystemA dedicated NVIDIA DGX Spark (GB10, 128 GB unified memory), reserved for you
Commercial modelHour, week, month or year. Hardware from €0.85/hour, managed service quoted per deployment
EndpointOpenAI-compatible /v1 on your own machine, only your traffic
LocationHosted in the EU: Málaga, Spain (eu-es-1)
Data handlingZero prompt retention, same policy as the serverless API

The model is validated on AxForge hardware and deployed on your system. After deployment, your endpoint speaks the OpenAI API with the model name you receive with the deployment.

Data & privacy

Zero prompt retention

Prompts and completions are processed in memory in the EU, not written to disk, not logged, not retained, and never used to train anything. We keep only request metadata (token counts, timestamps, status) for billing and operations. The full policy is at axforge.ai/privacy.

FAQ

LTX-2.5 hosting: common questions

Is LTX-2.5 on the AxForge serverless API?

Not on the serverless API. It is available as a managed deployment: validated on AxForge hardware and deployed on a DGX Spark for your traffic only. The serverless API serves Qwen3.8 27B.

What hardware does LTX-2.5 need?

A dedicated DGX Spark for the 19B Diffusers build; AxForge confirms resolution, clip length and throughput with the deployment.

Can it start from a still image?

Yes, image-to-video is what LTX-2.5 is built for; text-only prompting is also supported.

How fast is LTX-2.5 on your hardware?

We publish only numbers we measure ourselves, and we haven't benchmarked this model on our nodes yet. For quality benchmarks, see the official model card.

What does a dedicated LTX-2.5 deployment look like?

A dedicated NVIDIA DGX Spark (GB10, 128 GB unified memory) rented by the hour, week, month or year, running LTX-2.5 behind an OpenAI-compatible endpoint on your own machine, hosted in the EU with zero prompt retention.

What does EU LTX-2.5 hosting cost?

Hardware from €0.85/hour, by the hour, week, month or year; the managed service is quoted per deployment. Request deployment and we confirm both in writing.

Ready to build on EU inference?

Request deployment Talk to an engineer

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