LTX2.3 | 720x1280 | Local Inference Test & A 6-Month Silence
After a 6-month NDA project, a creator benchmarks the open-source LTX2.3 model, showing its local performance on consumer hardware.
After a mandatory six-month hiatus working on a professional, NDA-bound AI documentary project, a creator has returned to personal work with a performance benchmark of the open-source LTX2.3 image-to-video model. The test, conducted using a ComfyUI workflow on a high-end local workstation (AMD Ryzen 9 9950X, NVIDIA RTX 4090, 64GB DDR5 RAM), rendered a 720x1280 resolution video sequence. The first render took 315 seconds (just over 5 minutes), with a subsequent render dropping to 186 seconds (just over 3 minutes), showcasing potential caching or optimization benefits. The creator highlights LTX2.3's core advantage: its open-source nature allows for complete local inference, providing artists and commercial projects with greater control and independence from cloud-based AI video services.
The benchmark focused on the model's consistency in handling challenging visual elements like porcelain and metallic textures with complex light refraction. While the results are impressive for a local model on consumer hardware, the creator notes the presence of noticeable temporal artifacts and minor morphing upon close inspection. These are framed as acceptable trade-offs for the freedom of local execution. The creator announces the revival of their YouTube channel, promising future comparative analyses between LTX2.3, OpenAI's Veo 3.1, and other open and closed-source models, signaling a move towards transparent, data-driven evaluations in the AI video space.
- LTX2.3, an open-source image-to-video model, rendered a 720x1280 clip in 186-315 seconds on a local RTX 4090/ Ryzen 9 9950X rig.
- The creator emphasizes the model's value for commercial control, having just completed a 6-month NDA project using AI for a historical documentary.
- While the model handles complex textures well, it exhibits temporal artifacts, but these are deemed a fair trade-off for local, open-source execution.
Why It Matters
It proves high-quality AI video generation is becoming viable on local consumer hardware, reducing reliance on cloud APIs and giving creators more control.