Can an RTX 3070 with 8GB VRAM Run Wan2GP and LTX2? Expert Analysis

Can an RTX 3070 with 8GB VRAM Run Wan2GP and LTX2? Expert Analysis

As generative AI video tools rapidly evolve, users with mid-tier hardware face mounting pressure to upgrade or adapt. One such user, posting on the r/StableDiffusion subreddit, asked whether their NVIDIA RTX 3070 with 8GB VRAM and 16GB system RAM could handle Wan2GP — a GPU-accelerated AI video generator supporting models like LTX2, Flux, and Hunyuan Video — alongside their existing ComfyUI workflow for image generation. The question reflects a broader dilemma among AI enthusiasts: how to leverage next-generation models on aging hardware without sacrificing functionality.

According to the GitHub repository for Wan2GP, the project is designed as a lightweight, high-speed AI video generator optimized for consumer-grade GPUs. It explicitly supports Wan 2.1/2.2, LTX Video, and Flux, suggesting compatibility with the user’s target models. However, the repository provides no official hardware requirements, leaving users to infer performance thresholds from community benchmarks and similar tools.

Historically, AI video generation — particularly models like LTX2 — demands significantly more VRAM than image-only models such as Stable Diffusion. While Z-Image Turbo and Flux can operate within 8GB VRAM under optimized conditions, LTX2 and Wan2GP introduce temporal consistency, motion interpolation, and higher-resolution frame sequences that increase memory pressure. Early community benchmarks from Hugging Face and Stability AI forums indicate that LTX2 typically requires 12GB+ VRAM for 512x512 outputs at 16fps, and 16GB+ for 720p or higher. Even with quantization and memory offloading techniques, 8GB is considered the absolute lower bound — and even then, only with reduced resolution, frame rate, and batch size.

System RAM also presents a bottleneck. While VRAM handles model weights and latent tensors, system RAM manages data pipelines, preprocessing, and caching. With only 16GB of RAM, the user may encounter frequent swapping, especially when loading large video datasets or running ComfyUI alongside Wan2GP. Modern AI workflows often consume 8–12GB of system RAM just for model loading; leaving only 4–8GB for the OS and background tasks can result in instability or crashes.

That said, there are workarounds. Users have successfully run LTX2 variants on 8GB VRAM by employing techniques such as:

• Model quantization (e.g., converting FP16 to INT8 weights)
• Memory offloading (moving layers to CPU RAM during inference)
• Reducing context length (limiting video duration to 2–3 seconds)
• Lowering resolution (rendering at 384x384 instead of 512x512)

Additionally, Wan2GP’s architecture, as described in its GitHub documentation, prioritizes efficiency over raw fidelity — making it more viable than heavier alternatives like Sora or Pika. Users report marginal success on RTX 3060 12GB systems, suggesting that the 3070’s slightly better memory bandwidth and CUDA core count may offer a marginal advantage.

However, real-world usability remains questionable. For a user accustomed to smooth ComfyUI workflows with Z-Image Turbo, the transition to Wan2GP may introduce frustrating latency, frequent out-of-memory errors, and long render times — potentially negating the benefits of adopting the newer model. The project’s documentation does not guarantee stability on such hardware, and community feedback is sparse.

In conclusion, while technically possible under highly constrained conditions, running Wan2GP with LTX2 on an RTX 3070 and 16GB RAM is not recommended for productive or reliable use. It may serve as a proof-of-concept or experimental sandbox, but users seeking consistent performance should consider upgrading to at least a 12GB VRAM GPU and 32GB system RAM. For now, sticking with Flux and Z-Image Turbo remains the pragmatic choice.