LTX-2.3 inference slashed from 300s to 45s on RTX 3080Ti
INT8 quantization and resolution tweaks yield 6× speedup on older hardware.
A developer building an entertainment app powered by video generation AI successfully optimised LTX-2.3 inference speed on an RTX 3080Ti, dropping generation time from 300 seconds to just 45 seconds per clip. The process involved several key tweaks. First, reducing the output resolution from 1080×1920 to 720×1280 cut time to ~120s. Switching the spatial upscaler from 2× to 1.5× further reduced to 80s, but with a quality trade-off. Next, cutting Stage 2 (upsampling) steps from 3 to 2 by modifying the sigma list saved proportional time without noticeable quality loss. The biggest breakthrough came from using INT8 quantization instead of NVFP4—the RTX 3080Ti (Ampere) handles INT8 far better, dropping time from 80s to 45s. Since few INT8 models or ComfyUI nodes existed for the new v1.1, the developer wrote a custom conversion script and loader node using an AI agent.
The optimised workflow runs 832×1024 resolution at 49 frames (down from 121). Adding a LoRA (rank 16) adds about 4 seconds overhead. The first inference is slower due to model loading, but subsequent runs consistently achieve ~45s. For shorter clips, reducing frame count drastically cuts processing time. The developer notes that Sage Attention didn’t help on Ampere, but suggests RTX 50xx users may benefit. Training was done on an RTX 5090 with musubi-tuner using FP8 and NF4 to save VRAM. The final setup demonstrates that with careful tuning, high-quality video generation is practical even on last-gen consumer GPUs.
- Resolution drop from 1080×1920 to 832×1024 reduced time from 300s to ~120s.
- INT8 quantization provided the largest speedup, cutting from 80s to 45s on RTX 3080Ti.
- Custom ComfyUI nodes were required because few INT8 models existed for LTX-2.3 v1.1.
Why It Matters
Practical optimisation guide enables affordable video generation on consumer GPUs without sacrificing quality.