WanGP Runs Alibaba's Wan 2.1 Video AI on 6 GB GPUs

Alibaba’s Wan 2.1 video models typically need more GPU memory than a consumer laptop or older desktop card provides. The checkpoint hub addresses that constraint with smaller model files designed for WanGP, an open-source launcher that shifts selected workloads between GPU memory and system RAM. The repository shows 419,608 downloads in the last month, reflecting strong demand for local video generation on modest hardware.

The repository contains repackaged checkpoints rather than a new model architecture. Available files include GGUF Q4_K_M quantizations ranging from about 2.7 GB to 5.6 GB, along with ONNX and safetensors variants. Quantization stores weights at lower precision to reduce memory use, while WanGP adds offloading and scheduling so models can run when their weights and intermediate data do not fit in VRAM at once.

How WanGP fits the pieces together

WanGP combines a web interface, model downloader, inference runtime, memory manager, and postprocessing tools. Its reported memory reduction can approach 50 percent for some workloads, including runs that retain the checkpoint’s original precision. A 12 GB RTX 3060 or 8 GB RTX 2060 Super can therefore attempt jobs that would otherwise require a higher-memory card, although lower VRAM increases reliance on slower transfers through system RAM.

The project reports that a 14-billion-parameter Wan model can generate as many as 128 frames, or about eight seconds of video, with 12 GB of VRAM. A five-second 480p run can fit within 8 GB during most of the pipeline, but the variational autoencoder, or VAE, may need as much as 12 GB while encoding or decoding frames. That spike can slow the beginning and end of a run or cause an out-of-memory failure.