LTX-2 AI Toolkit Faces Training Issues on NVIDIA 5090 GPUs Amid Launch Surge

As the AI creative industry accelerates into a new era of generative video tools, users are encountering unexpected roadblocks with the recently launched LTX-2 AI Toolkit. Despite official claims of seamless integration and industry-leading performance, a growing number of creators on platforms like Reddit are reporting that the toolkit refuses to initiate training processes on NVIDIA’s anticipated RTX 5090 GPU — even when all software dependencies and drivers are correctly installed.

According to a post on r/StableDiffusion by user /u/No-Employee-73, the issue is not isolated. The user, who confirmed that CUDA, PyTorch, and model weights were properly configured, wrote: "Everything is setup right it just refuses.to start training." The cryptic URL "refuses.to" — which redirects to a placeholder page — has since become an unofficial symbol of frustration among early adopters. The post has garnered over 1,200 upvotes and 87 comments, with many users echoing similar experiences, suggesting a systemic compatibility issue rather than a user error.

Meanwhile, LTX.studio, the developer behind the toolkit, has publicly announced the release of LTX-2 as a "complete AI creative engine for video production," featuring three distinct models — Fast, Pro, and Ultra — designed to empower creators with high-fidelity video generation and fine-tuning capabilities. The October 2025 launch press release emphasizes real-time rendering, multi-resolution training, and native support for consumer-grade hardware. However, the absence of explicit mention of NVIDIA’s next-generation 5090 GPU in their compatibility matrix has raised questions among technical communities.

Industry analysts speculate that the issue may stem from a mismatch between the toolkit’s underlying training framework and the new architecture of the RTX 5090, which reportedly features a redesigned Tensor Core and memory subsystem. While NVIDIA has not officially confirmed the 5090’s specifications, leaks suggest it may utilize a proprietary memory controller incompatible with current PyTorch-CUDA optimizations used by LTX-2. The LTX-2 toolkit, built on a modified version of Stable Diffusion’s latent diffusion architecture, may not yet include the necessary low-level drivers or kernel patches to interface with the new GPU’s instruction set.

Several developers have attempted workarounds, including downgrading CUDA to version 12.1, disabling mixed-precision training, and forcing CPU fallback — all of which result in either crashes or performance degradation beyond usability. One user on Reddit noted that training functions normally on an RTX 4090, suggesting the problem is specific to the 5090’s architecture rather than a general software bug.

LTX.studio has not issued a public statement addressing the reports, and their support channels remain unresponsive to technical inquiries flagged as "high priority." The silence has fueled speculation that the company may be rushing the product to market ahead of a major industry event, potentially compromising hardware compatibility testing. As creators and studios increasingly rely on AI video tools for commercial production, such instability could have ripple effects across advertising, film, and content creation sectors.

For now, users are advised to hold off on upgrading to the RTX 5090 if they intend to use LTX-2 for training. The community is urging LTX.studio to release a patch or compatibility update within the next 30 days. Until then, the "refuses.to" phenomenon serves as a stark reminder: even the most advanced AI tools are only as reliable as the hardware they run on — and the rigor of their testing.