Datasheet for local AI49 models98 GPUsData read 2026-09-25
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Qwen-Image 2.1 VRAM requirements

September 2026: a new 7B model that does text-to-image and editing in one, with transparent-background (RGBA) output. Far lighter than the 20B Qwen-Image.

Released 2026-09Licence: Qwen Research License (non-commercial research license)Steps: 20–40Text encoder: Qwen3-VL 8B (9.4 GB as INT8)
TypeImage
Parameters7.1B
Fits entirely from6 GB
8-bit or better from10 GB

01The files, and how much VRAM each needs

FileSizeNeededMin. VRAMQualitySource
16-bit14.2 GB16.5 GB17 GBthe original weightsComfy-Org/Qwen-Image-2.1 →
Q8_07.6 GB9.9 GB10 GBpractically identical to the originalAbiray/Qwen-Image-2.1-GGUF →
INT87.3 GB9.6 GB10 GBpractically identical to the originalComfy-Org/Qwen-Image-2.1 →
Q6_K5.9 GB8.2 GB9 GBvery close to the originalAbiray/Qwen-Image-2.1-GGUF →
Q5_K_M5.0 GB7.3 GB8 GBclose; small differences in fine detailAbiray/Qwen-Image-2.1-GGUF →
Q4_K_M4.2 GB6.5 GB7 GBgood; some loss in fine detail and textAbiray/Qwen-Image-2.1-GGUF →
Q3_K_M3.2 GB5.5 GB6 GBnoticeable loss of detailAbiray/Qwen-Image-2.1-GGUF →

“Needed” = file + 1.5 GB working memory + 0.8 GB system reserve.

03Best GPU for Qwen-Image 2.1

The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for Qwen-Image 2.1 → Planning bigger images or longer clips? Open the calculator →

04By graphics card

GPUVRAMVerdictBest fileNeeded
Desktop graphics cards
RTX 2060 6 GB6 GBTightQ3_K_M5.5 GB
RTX 3050 6 GB6 GBTightQ3_K_M5.5 GB
RTX 2070 Super 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 2080 Super 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3050 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3060 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3060 Ti 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3070 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3070 Ti 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 4060 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 4060 Ti 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5050 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5060 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5060 Ti 8 GB8 GBRunsQ5_K_M7.3 GB
RX 7600 8 GB8 GBRunsQ5_K_M7.3 GB
RX 9050 8 GB8 GBRunsQ5_K_M7.3 GB
RX 9060 XT 8 GB8 GBRunsQ5_K_M7.3 GB
Arc B570 10 GB10 GBRuns wellQ8_09.9 GB
RTX 3080 10 GB10 GBRuns wellQ8_09.9 GB
RTX 2080 Ti 11 GB11 GBRuns wellQ8_09.9 GB
Arc B580 12 GB12 GBRuns wellQ8_09.9 GB
RTX 2060 12 GB12 GBRuns wellQ8_09.9 GB
RTX 3060 12 GB12 GBRuns wellQ8_09.9 GB
RTX 3080 12 GB12 GBRuns wellQ8_09.9 GB
RTX 3080 Ti 12 GB12 GBRuns wellQ8_09.9 GB
RTX 4070 12 GB12 GBRuns wellQ8_09.9 GB
RTX 4070 Super 12 GB12 GBRuns wellQ8_09.9 GB
RTX 4070 Ti 12 GB12 GBRuns wellQ8_09.9 GB
RTX 5070 12 GB12 GBRuns wellQ8_09.9 GB
RX 7700 XT 12 GB12 GBRuns wellQ8_09.9 GB
RX 9070 GRE 12 GB12 GBRuns wellQ8_09.9 GB
Arc A770 16 GB16 GBRuns wellQ8_09.9 GB
RTX 4060 Ti 16 GB16 GBRuns wellQ8_09.9 GB
RTX 4070 Ti Super 16 GB16 GBRuns wellQ8_09.9 GB
RTX 4080 16 GB16 GBRuns wellQ8_09.9 GB
RTX 4080 Super 16 GB16 GBRuns wellQ8_09.9 GB
RTX 5060 Ti 16 GB16 GBRuns wellQ8_09.9 GB
RTX 5070 Ti 16 GB16 GBRuns wellQ8_09.9 GB
RTX 5080 16 GB16 GBRuns wellQ8_09.9 GB
RX 7600 XT 16 GB16 GBRuns wellQ8_09.9 GB
RX 7800 XT 16 GB16 GBRuns wellQ8_09.9 GB
RX 7900 GRE 16 GB16 GBRuns wellQ8_09.9 GB
RX 9060 XT 16 GB16 GBRuns wellQ8_09.9 GB
RX 9070 16 GB16 GBRuns wellQ8_09.9 GB
RX 9070 XT 16 GB16 GBRuns wellQ8_09.9 GB
RX 7900 XT 20 GB20 GBRuns well16-bit16.5 GB
Arc Pro B60 24 GB24 GBRuns well16-bit16.5 GB
RTX 3090 24 GB24 GBRuns well16-bit16.5 GB
RTX 3090 Ti 24 GB24 GBRuns well16-bit16.5 GB
RTX 4090 24 GB24 GBRuns well16-bit16.5 GB
RX 7900 XTX 24 GB24 GBRuns well16-bit16.5 GB
Arc Pro B70 32 GB32 GBRuns well16-bit16.5 GB
RTX 5090 32 GB32 GBRuns well16-bit16.5 GB
Laptop GPUs
RTX 3050 Laptop 4 GB4 GBOffload onlyQ3_K_M5.5 GB
RTX 3050 Ti Laptop 4 GB4 GBOffload onlyQ3_K_M5.5 GB
RTX 2060 Laptop 6 GB6 GBTightQ3_K_M5.5 GB
RTX 3050 Laptop 6 GB6 GBTightQ3_K_M5.5 GB
RTX 3060 Laptop 6 GB6 GBTightQ3_K_M5.5 GB
RTX 4050 Laptop 6 GB6 GBTightQ3_K_M5.5 GB
RTX 2070 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 2070 Super Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 2080 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 2080 Super Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3070 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3070 Ti Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 3080 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 4060 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 4070 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5050 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5060 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 5070 Laptop 8 GB8 GBRunsQ5_K_M7.3 GB
RX 7600M 8 GB8 GBRunsQ5_K_M7.3 GB
RX 7600M XT 8 GB8 GBRunsQ5_K_M7.3 GB
RX 7600S 8 GB8 GBRunsQ5_K_M7.3 GB
RX 7700S 8 GB8 GBRunsQ5_K_M7.3 GB
RTX 4080 Laptop 12 GB12 GBRuns wellQ8_09.9 GB
RTX 5070 Laptop 12 GB12 GBRuns wellQ8_09.9 GB
RTX 5070 Ti Laptop 12 GB12 GBRuns wellQ8_09.9 GB
RX 7800M 12 GB12 GBRuns wellQ8_09.9 GB
RTX 3080 Laptop 16 GB16 GBRuns wellQ8_09.9 GB
RTX 3080 Ti Laptop 16 GB16 GBRuns wellQ8_09.9 GB
RTX 4090 Laptop 16 GB16 GBRuns wellQ8_09.9 GB
RTX 5080 Laptop 16 GB16 GBRuns wellQ8_09.9 GB
RX 7900M 16 GB16 GBRuns wellQ8_09.9 GB
RTX 5090 Laptop 24 GB24 GBRuns well16-bit16.5 GB
Unified memory
Radeon 8060S (Strix Halo) 96 GB96 GBRuns well16-bit16.5 GB
Apple Silicon Macs (by memory)
Mac 16 GB12.7 GBRuns wellQ8_09.9 GB
Mac 18 GB14.4 GBRuns wellQ8_09.9 GB
Mac 24 GB19.6 GBRuns well16-bit16.5 GB
Mac 32 GB26.8 GBRuns well16-bit16.5 GB
Mac 36 GB30.2 GBRuns well16-bit16.5 GB
Mac 48 GB40.2 GBRuns well16-bit16.5 GB
Mac 64 GB55.7 GBRuns well16-bit16.5 GB
Mac 96 GB85 GBRuns well16-bit16.5 GB
Mac 128 GB115.4 GBRuns well16-bit16.5 GB
Mac 192 GB175.4 GBRuns well16-bit16.5 GB
Mac 256 GB236.9 GBRuns well16-bit16.5 GB
Mac 512 GB498.1 GBRuns well16-bit16.5 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

FileFolderSizeWhen
Qwen3-VL 8B INT8
qwen3vl_8b_int8_convrot.safetensors
models/text_encoders9.4 GBtext encoder · smaller, recommendedDownload →
Qwen3-VL 8B BF16
qwen3vl_8b_bf16.safetensors
models/text_encoders17.5 GBtext encoder · alternativeDownload →
Qwen-Image 2.1 VAE
qwen_image_2.1_vae_bf16.safetensors
models/vae0.7 GBrequiredDownload →

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.

Qwen3-VL 8B: 17.5 GB as 16-bit, 9.4 GB as INT8. 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 INT8 encoder fits on its own, so prompt encoding stays fast.

06Where the files go in ComfyUI

FileFolderLoader node
Diffusion model (.safetensors: 16-bit, FP8, INT8)ComfyUI/models/diffusion_modelsLoad Diffusion Model
GGUF file (.gguf)ComfyUI/models/unetUnet Loader (GGUF) — from the ComfyUI-GGUF node pack
Text encoderComfyUI/models/text_encodersLoad CLIP / DualCLIPLoader (or the GGUF versions)
VAEComfyUI/models/vaeLoad 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 typeRTX 50RTX 40RTX 30 / 20RX 9000RX 7000/6000 · Strix HaloIntel Arc
16-bitRunsRunsRunsRunsRunsRuns
INT8Native INT8Native INT8Native INT8Native INT8Native INT8No INT8 speed-up
GGUFRuns (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

LabelGPUSetupResultPeak VRAMDateSource
reportedRTX 4060 Ti 16 GBQwen Image 2.1 INT8 Convrot (+Qwen3 VL 8B INT8 TE)
“Image generation: ~20 seconds ... Peak VRAM: ~15 GB”
ComfyUI, no CPU/disk offload; approximate values ('~'); editing ~60 s; resolution/steps not stated; date derived from '1 day ago' on 2026-09-25
20 s / image15 GB2026-09-24huggingface.co →
reportedRTX 4070 12 GBQwen-Image-2.1 int8 convrot + qwen3vl_8b_int8 convrot TE + VAE bf16 · 1024x1024 · 25 steps
“1024x1024・25ステップ(公式ワークフロー相当) | 14.0 秒”
ComfyUI, RTX 4070 12GB with partial offload (17GB weights); same table: 12 steps 9.6 s, 2048x2048 20 steps 90.5 s; repo date not shown (Qwen-Image 2.1 era, 2026)
14 s / image——github.com →
reportedRTX 4070 12 GBQwen Image 2.1 INT8 ConvRot · 832x1248 · 25 steps
“「全体の所要時間」で見ると、平均で32.63秒から19.83秒へ(約39.2%短縮)… GPU全体のVRAMピーク:11.19 GiB → 11.28 GiB”
ComfyUI, RTX 4070 12GB, Euler/simple CFG 1; 32.63 s = standard total time (19.83 s with Spectrum speedup node); VRAM in GiB
32.63 s / image11.19 GB2026-09-24note.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 2.1

No trainer documentation with a VRAM figure for this model was found yet. What the trainers say for other models →

10Every file tracked for Qwen-Image 2.1

FileTypeSizeRepo
qwen_image_2.1_Q8_0.ggufQ8_07.59 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_Q6_K.ggufQ6_K5.88 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_Q5_K_M.ggufQ5_K_M5.01 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_Q4_K_M.ggufQ4_K_M4.19 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_Q4_K_S.ggufQ4_K_S4.06 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_Q3_K_M.ggufQ3_K_M3.19 GBAbiray/Qwen-Image-2.1-GGUF →
qwen_image_2.1_bf16.safetensorsBF1614.23 GBComfy-Org/Qwen-Image-2.1 →
qwen_image_2.1_int8_convrot.safetensorsINT87.26 GBComfy-Org/Qwen-Image-2.1 →
qwen-image-2.1-F16.ggufF1614.23 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q8_0.ggufQ8_07.64 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q6_K_XL.ggufQ6_K_XL6.72 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q6_K.ggufQ6_K6.27 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q5_K_M.ggufQ5_K_M5.39 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q5_K_S.ggufQ5_K_S4.50 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q4_K_M.ggufQ4_K_M4.20 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q4_K_S.ggufQ4_K_S3.91 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q3_K_XL.ggufQ3_K_XL3.61 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q3_K_M.ggufQ3_K_M3.17 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q3_K_S.ggufQ3_K_S2.72 GBunsloth/Qwen-Image-2.1-GGUF →
qwen-image-2.1-Q2_K.ggufQ2_K2.47 GBunsloth/Qwen-Image-2.1-GGUF →

New 7B single-stream DiT (HF API 7.1B) - much smaller than the 20B Qwen-Image; one model does text-to-image AND editing, with native RGBA transparency. Optional prompt-enhancer LLM (Qwen3.5-9B int8_convrot, 9471072252 each for t2i/i2i) used via TextGenerate node. unsloth/Qwen-Image-2.1-FP8 (Qwen-Image-2.1-FP8.safetensors 7122877560) exists but is not documented for ComfyUI, so not listed as a file.