Local Ai
how much of vram i need for joy-image-edit
This r/StableDiffusion thread discusses the VRAM requirements for running Qwen-Image-Edit (joy-image-edit) locally. The full model in standard precision demands significant VRAM — using bitsandbytes q
This r/StableDiffusion thread discusses the VRAM requirements for running Qwen-Image-Edit (joy-image-edit) locally. The full model in standard precision demands significant VRAM — using bitsandbytes quantization requires just over 17GB on a GPU like the RTX 3090, while the TorchAO variant uses approximately 22–23GB — and the FP8 workflow is generally recommended for at least an RTX 4090 with 24GB VRAM . For lower-end hardware, community-provided GGUF quantized versions (q4_0, q8_0, etc.) can run on 8GB VRAM devices , and per-layer CPU offloading via tools like Nunchaku can reduce requirements to as little as 3–4GB of VRAM .
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Source: r/StableDiffusion | 2026-04-12