Datasheet for local AI50 models99 GPUsData read 2026-10-01
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Ming-Image 0.1 Design on 6 GB of VRAM

Image model · 6.15BAny 6 GB GPUData 2026-10-01
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Not entirely in VRAM, but it can still run.

Nothing fits entirely, but Q3_K_M (5.1 GB) overflows by only about 1.4 GB. ComfyUI keeps that part in system RAM automatically: slower than a full fit, but usable.

Best fileQ3_K_M
File size5.1 GB
VRAM needed~7.4 GB
System RAM32 GB+
Memory map · 6 GB GPU7.4 GB needed · 1.4 GB over 6 GB
04 GB7 GB
Weights Q3_K_M · 5.1 GBWorking memory · 1.5 GBReserve · 0.8 GBSpills to system RAM · 1.4 GB
calculated from real file sizes plus working memory. Not yet measured on this setup. How this works.

Worth trying on NVIDIA GPUs: with an up-to-date ComfyUI, Dynamic VRAM (on by default for NVIDIA since March 2026) streams whatever does not fit from system RAM, and ComfyUI's own start-up message recommends native FP8/INT8 files over GGUF, saying they “will be faster even if they are larger than your memory”. So before settling for a heavily compressed GGUF, try the native INT8 file (6.2 GB). The file recommended above is the best one that fits entirely — the safe choice on older ComfyUI versions, AMD and Intel. Comfy blog: Dynamic VRAM →

01What to download

PartFileFolderSize
ModelMing-Image-0.1-Design-Q3_K_M.gguf
Q3_K_M · realrebelai/Ming-Image_GGUFs
models/unet5.1 GBDownload →
Text encoderming_image_0.1_ling_mini_2.0_int8_convrot.safetensors
Ling-Mini-2.0 INT8 · Comfy-Org/Ming-Image
models/text_encoders19.5 GBDownload →
VAEming_image_vae_bf16.safetensors
Ming-Image VAE · Comfy-Org/Ming-Image
models/vae0.3 GBDownload →
Total download · keep about the same free on disk24.9 GB

Alternatives: Ling-Mini-2.0 BF16 (36.7 GB); Ling-Mini-2.0 (4-bit w4a8) (12.8 GB). The 16-bit text encoder is a little more faithful; the smaller one is chosen here because it loads faster and needs less RAM. Sizes read from Hugging Face (2026-10-01). “Download” links start the file directly; the file name links are the same files the official ComfyUI workflows use.

Free ComfyUI workflow, tested on my RTX 5060 Ti: INT8 workflow (.json). Drag the file into ComfyUI. All workflows →

System RAM: 32 GB or more. ComfyUI keeps the model file, the text encoder and the VAE in system RAM and moves them to the GPU as needed. With this set of files that is about 24.9 GB, plus roughly 6 GB for Windows, ComfyUI and a browser: 30.9 GB in total. With less RAM it still runs, but Windows starts swapping to disk and loading gets very slow. calculated

02Every file, on 6 GB

FileSizeNeededOn this cardQualityDownload
16-bit
SAFETENSORS · Comfy-Org
12.3 GB14.6 GB8.6 GB too bigthe original weightsHugging Face →
Q8_0
GGUF · realrebelai
7.3 GB9.6 GB3.6 GB too bigpractically identical to the originalHugging Face →
INT8
SAFETENSORS · Comfy-Org
6.2 GB8.5 GB2.5 GB too bigpractically identical to the originalHugging Face →
Q6_K
GGUF · realrebelai
6.8 GB9.1 GB3.1 GB too bigvery close to the originalHugging Face →
Q5_K_M
GGUF · realrebelai
6.2 GB8.5 GB2.5 GB too bigclose; small differences in fine detailHugging Face →
Q4_K_M
GGUF · realrebelai
5.9 GB8.2 GB2.2 GB too biggood; some loss in fine detail and textHugging Face →
Q3_K_M ←
GGUF · realrebelai
5.1 GB7.4 GBspills 1.4 GBnoticeable loss of detailHugging Face →
Q2_K
GGUF · realrebelai
4.7 GB7.0 GBspills 1.0 GBheavy loss; a last resortHugging Face →

Sizes from the Hugging Face file listing, read 2026-10-01. “Needed” = file + 1.5 GB working memory for this model + 0.8 GB kept free for the system. Made for 2048×2048 as well as 1024×1024; at 2048 the working memory is several times larger than the figure used here. A separate "layer" checkpoint of the same size splits a design into layers. Native in ComfyUI since v0.38.0 (late September 2026).

03The text encoder

Ling-Mini-2.0 (a large language model): 36.7 GB as 16-bit, 19.5 GB as INT8, 12.8 GB as 4-bit w4a8 (GGUF versions from realrebelai: Q4_K_M 11.5 GB, Q2_K 7.9 GB). ComfyUI encodes the prompt first and can push the encoder out of VRAM before sampling, so it does not have to fit together with the model. On 6 GB the encoder itself is too big for VRAM, so let it run from system RAM: slower prompt encoding, same images.