Qwen-Image VRAM requirements
Alibaba's 20B text-to-image model, known for rendering long text and posters well. Big: the 8-bit file alone is over 20 GB.
01The files, and how much VRAM each needs
| File | Size | Needed | Min. VRAM | Quality | Source |
|---|---|---|---|---|---|
| 16-bit | 40.9 GB | 43.7 GB | 44 GB | the original weights | Comfy-Org/Qwen-Image_ComfyUI → |
| Q8_0 | 21.8 GB | 24.6 GB | 25 GB | practically identical to the original | city96/Qwen-Image-gguf → |
| FP8 | 20.4 GB | 23.2 GB | 24 GB | practically identical to the original | Comfy-Org/Qwen-Image_ComfyUI → |
| Q6_K | 16.8 GB | 19.6 GB | 20 GB | very close to the original | city96/Qwen-Image-gguf → |
| Q5_K_M | 14.9 GB | 17.7 GB | 18 GB | close; small differences in fine detail | city96/Qwen-Image-gguf → |
| Q4_K_M | 13.1 GB | 15.9 GB | 16 GB | good; some loss in fine detail and text | city96/Qwen-Image-gguf → |
| Q3_K_M | 9.7 GB | 12.5 GB | 13 GB | noticeable loss of detail | city96/Qwen-Image-gguf → |
| Q2_K | 7.1 GB | 9.9 GB | 10 GB | heavy loss; a last resort | city96/Qwen-Image-gguf → |
“Needed” = file + 2 GB working memory + 0.8 GB system reserve.
02By amount of VRAM
03Best GPU for Qwen-Image
The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for Qwen-Image → Planning bigger images or longer clips? Open the calculator →
04By graphics card
| GPU | VRAM | Verdict | Best file | Needed |
|---|---|---|---|---|
| Desktop graphics cards | ||||
| RTX 2060 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 3050 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 2070 Super 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 2080 Super 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3050 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3060 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3070 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3070 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 4060 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 4060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5050 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5060 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5060 Ti 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 7600 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 9050 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 9060 XT 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| Arc B570 10 GB | 10 GB | Tight | Q2_K | 9.9 GB |
| RTX 3080 10 GB | 10 GB | Tight | Q2_K | 9.9 GB |
| RTX 2080 Ti 11 GB | 11 GB | Tight | Q2_K | 9.9 GB |
| Arc B580 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 2060 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 3060 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 3080 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 3080 Ti 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 4070 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 4070 Super 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 4070 Ti 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 5070 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RX 7700 XT 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RX 9070 GRE 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| Arc A770 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 4060 Ti 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 4070 Ti Super 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 4080 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 4080 Super 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 5060 Ti 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 5070 Ti 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 5080 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 7600 XT 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 7800 XT 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 7900 GRE 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 9060 XT 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 9070 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 9070 XT 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 7900 XT 20 GB | 20 GB | Runs | Q6_K | 19.6 GB |
| Arc Pro B60 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| RTX 3090 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| RTX 3090 Ti 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| RTX 4090 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| RX 7900 XTX 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| Arc Pro B70 32 GB | 32 GB | Runs well | Q8_0 | 24.6 GB |
| RTX 5090 32 GB | 32 GB | Runs well | FP8 | 23.2 GB |
| Laptop GPUs | ||||
| RTX 3050 Laptop 4 GB | 4 GB | Not practical | Q2_K | 9.9 GB |
| RTX 3050 Ti Laptop 4 GB | 4 GB | Not practical | Q2_K | 9.9 GB |
| RTX 2060 Laptop 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 3050 Laptop 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 3060 Laptop 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 4050 Laptop 6 GB | 6 GB | Not practical | Q2_K | 9.9 GB |
| RTX 2070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 2070 Super Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 2080 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 2080 Super Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3070 Ti Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 3080 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 4060 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 4070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5050 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5060 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 5070 Laptop 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 7600M 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 7600M XT 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 7600S 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RX 7700S 8 GB | 8 GB | Offload only | Q2_K | 9.9 GB |
| RTX 4080 Laptop 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 5070 Laptop 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 5070 Ti Laptop 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RX 7800M 12 GB | 12 GB | Tight | Q2_K | 9.9 GB |
| RTX 3080 Laptop 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 3080 Ti Laptop 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 4090 Laptop 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 5080 Laptop 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RX 7900M 16 GB | 16 GB | Runs | Q4_K_M | 15.9 GB |
| RTX 5090 Laptop 24 GB | 24 GB | Runs well | FP8 | 23.2 GB |
| Unified memory | ||||
| Radeon 8060S (Strix Halo) 96 GB | 96 GB | Runs well | 16-bit | 43.7 GB |
| Apple Silicon Macs (by memory) | ||||
| Mac 16 GB | 12.7 GB | Tight | Q3_K_M | 12.5 GB |
| Mac 18 GB | 14.4 GB | Tight | Q3_K_M | 12.5 GB |
| Mac 24 GB | 19.6 GB | Runs | Q5_K_M | 17.7 GB |
| Mac 32 GB | 26.8 GB | Runs well | Q8_0 | 24.6 GB |
| Mac 36 GB | 30.2 GB | Runs well | Q8_0 | 24.6 GB |
| Mac 48 GB | 40.2 GB | Runs well | Q8_0 | 24.6 GB |
| Mac 64 GB | 55.7 GB | Runs well | 16-bit | 43.7 GB |
| Mac 96 GB | 85 GB | Runs well | 16-bit | 43.7 GB |
| Mac 128 GB | 115.4 GB | Runs well | 16-bit | 43.7 GB |
| Mac 192 GB | 175.4 GB | Runs well | 16-bit | 43.7 GB |
| Mac 256 GB | 236.9 GB | Runs well | 16-bit | 43.7 GB |
| Mac 512 GB | 498.1 GB | Runs well | 16-bit | 43.7 GB |
On RTX 40/50 GPUs the FP8 file is preferred over Q8_0 when both fit (hardware FP8). All verdicts are calculated; see how the numbers work.
05Text encoder, VAE and other files
| File | Folder | Size | When | |
|---|---|---|---|---|
| Qwen2.5-VL 7B FP8 qwen_2.5_vl_7b_fp8_scaled.safetensors | models/text_encoders | 9.4 GB | text encoder · smaller, recommended | Download → |
| Qwen2.5-VL 7B BF16 qwen_2.5_vl_7b.safetensors | models/text_encoders | 16.6 GB | text encoder · alternative | Download → |
| Qwen-Image VAE qwen_image_vae.safetensors | models/vae | 0.3 GB | required | Download → |
The files the official ComfyUI workflows load next to the model. Sizes read from Hugging Face (2026-09-25). Every GPU page for this model lists the exact set to download for that card, with the total.
Qwen2.5-VL 7B: 16.6 GB as 16-bit, 9.4 GB as FP8. 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 16 GB the FP8 encoder fits on its own, so prompt encoding stays fast.
06Where the files go in ComfyUI
| File | Folder | Loader node |
|---|---|---|
| Diffusion model (.safetensors: 16-bit, FP8, INT8) | ComfyUI/models/diffusion_models | Load Diffusion Model |
| GGUF file (.gguf) | ComfyUI/models/unet | Unet Loader (GGUF) — from the ComfyUI-GGUF node pack |
| Text encoder | ComfyUI/models/text_encoders | Load CLIP / DualCLIPLoader (or the GGUF versions) |
| VAE | ComfyUI/models/vae | Load VAE |
Standard ComfyUI folders. After copying files, press R in ComfyUI (or restart it) to refresh the lists. Some uploads need their uploader's own loader node — see the notes above.
07AMD, Intel and NVIDIA: which file types are fast
| File type | RTX 50 | RTX 40 | RTX 30 / 20 | RX 9000 | RX 7000/6000 · Strix Halo | Intel Arc |
|---|---|---|---|---|---|---|
| 16-bit | Runs | Runs | Runs | Runs | Runs | Runs |
| FP8 | Native FP8 | Native FP8 | No FP8 speed-up | Native FP8 | No FP8 speed-up | No FP8 speed-up |
| GGUF | Runs (GGUF node) | Runs (GGUF node) | Runs (GGUF node) | Runs (GGUF node) | Runs (GGUF node) | Runs (GGUF node) |
Every file type loads on every listed GPU, so the memory verdicts apply to all of them. What differs is speed: FP8 maths needs RTX 40/50 or RX 9000 (with ROCm 6.4+ and PyTorch 2.7+); ComfyUI's INT8 maths runs on NVIDIA and AMD, not on Intel; GGUF is unpacked on the fly on any GPU, which costs some speed. NVFP4 files are fast only on RTX 50. AMD runs ComfyUI on Windows through ROCm, Intel through PyTorch XPU; some custom nodes are NVIDIA-only. Source: ComfyUI model_management.py. AMD and Intel guide →
08Measured and reported results
| Label | GPU | Setup | Result | Peak VRAM | Date | Source |
|---|---|---|---|---|---|---|
| reported | Radeon 8060S (Strix Halo) 96 GB | Qwen-Image-2512 BF16 + 4-step Lightning LoRA · 1328x1328 · 4 steps “"workflow": "Qwen-Image-2512-BF16-4-Step-LoRA.json", ... "duration_seconds": 75.37661480903625” kyuz0 Strix Halo ComfyUI toolbox benchmark (Ryzen AI Max, ROCm); cold run incl. model load, flags --disable-mmap --gpu-only --disable-smart-memory --cache-none; resolution from ben | 75.38 s / image | — | 2026-02-13 | raw.githubusercontent.com → |
| reported | RTX 3060 12 GB | 20 steps “Qwen-ImageがRTX 3060(12GB)で動くと聞いて、早速ComfyUI版をお試し。確かに問題なく動いて、20stepでちょうど5分。” 'ちょうど5分' = exactly 5 minutes (converted to 300 s); ComfyUI version, file/resolution not stated; quote taken from search index (x.com not fetchable); date from tweet ID | 300 s / image | — | 2025-08-05 | x.com → |
Reported results are other people's numbers, copied as published, with a link. Settings, drivers and ComfyUI versions differ, so compare them with care. Send yours.
09Training a LoRA for Qwen-Image
| Trainer | VRAM | Type | Settings and quote | Source |
|---|---|---|---|---|
| musubi-tuner | 12 GB | stated minimum | 1024x1024, batch 1, bf16 mixed precision, gradient checkpointing, xformers, --fp8_base --fp8_scaled + --blocks_to_swap 45; 64GB RAM recommended. Table: none 42GB, fp8 30GB, +swap16 24GB “+ --blocks_to_swap 45|12GB” | github.com → |
| OneTrainer | 16 GB | example run | Official preset: 512px, batch 2, transformer fp8, TE fp8, layer offload fraction 0.5 (24GB preset uses 0.1) “#qwen LoRA 16GB.json” | github.com → |
| ai-toolkit | 24 GB | example run | Official example config: 3-bit (uint3) base with accuracy recovery adapter, fp8 TE, cached text embeddings, low_vram, rank 16, batch 1, gradient checkpointing “# 3bit is required for 24GB” | github.com → |
| diffusion-pipe | 24 GB | stated minimum | Example config: fp8 transformer, blocks_to_swap 8, rank 32, activation checkpointing, 640px dataset suggested, expandable_segments “You will need block swapping. See the [example 24GB VRAM config]” | github.com → |
| SimpleTuner | 24 GB | stated minimum | int2-quanto or nf4-bnb base, batch 1, gradient checkpointing, LoRA rank 1-8, 512-768px start; 40GB+ strongly recommended “A 24GB GPU is the absolute minimum, and even then you'll need extensive quantization and careful configuration.” | github.com → |
reported Figures as stated by each trainer's own documentation or official example configs, read 2026-09-25. “Stated minimum” = the docs call it a minimum; “example run” = a config or measured run at that size. They differ a lot because of settings: an 8-bit or 4-bit base model, block swapping and lower resolution all cut memory. All models →
10Every file tracked for Qwen-Image
| File | Type | Size | Repo |
|---|---|---|---|
| qwen_image_bf16.safetensors (original) | BF16 | 40.86 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_2512_bf16.safetensors (2512) | BF16 | 40.86 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_fp8_hq.safetensors (original) | FP8 | 22.74 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_fp8mixed.safetensors (original) | FP8 | 20.53 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_2512_fp8_e4m3fn.safetensors (2512) | FP8 | 20.43 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_fp8_e4m3fn.safetensors (original) | FP8 | 20.43 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen_image_nvfp4.safetensors (original) | NVFP4 | 19.77 GB | Comfy-Org/Qwen-Image_ComfyUI → |
| qwen-image-BF16.gguf | BF16 | 40.87 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q8_0.gguf | Q8_0 | 21.76 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q6_K.gguf | Q6_K | 16.82 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q5_1.gguf | Q5_1 | 15.39 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q5_K_M.gguf | Q5_K_M | 14.93 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q5_0.gguf | Q5_0 | 14.40 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q5_K_S.gguf | Q5_K_S | 14.12 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q4_K_M.gguf | Q4_K_M | 13.07 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q4_1.gguf | Q4_1 | 12.84 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q4_K_S.gguf | Q4_K_S | 12.14 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q4_0.gguf | Q4_0 | 11.85 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q3_K_M.gguf | Q3_K_M | 9.68 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q3_K_S.gguf | Q3_K_S | 8.95 GB | city96/Qwen-Image-gguf → |
| qwen-image-Q2_K.gguf | Q2_K | 7.06 GB | city96/Qwen-Image-gguf → |
MMDiT 20B. Text encoder Qwen2.5-VL-7B (see qwen2.5-vl-7b entry). Qwen-Image-2512 is an updated checkpoint of the same architecture (same sizes). nvfp4 needs RTX 50-series for speedup.