Datasheet for local AI49 models98 GPUsData read 2026-09-25
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FLUX.2 [klein] 4B VRAM requirements

The smallest FLUX.2: 4B parameters, Apache-2.0 licence, a few steps per image. Fits almost any modern card.

Released 2026-01Licence: Apache-2.0Steps: 4Text encoder: Qwen3 4B (5.6 GB as FP8)
TypeImage
Parameters4B
Fits entirely from4 GB
8-bit or better from6 GB

01The files, and how much VRAM each needs

FileSizeNeededMin. VRAMQualitySource
16-bit7.8 GB9.6 GB10 GBthe original weightsblack-forest-labs/FLUX.2-klein-4B →
Q8_04.3 GB6.1 GB7 GBpractically identical to the originalunsloth/FLUX.2-klein-4B-GGUF →
FP84.1 GB5.9 GB6 GBpractically identical to the originalblack-forest-labs/FLUX.2-klein-4b-fp8 →
Q6_K3.4 GB5.2 GB6 GBvery close to the originalunsloth/FLUX.2-klein-4B-GGUF →
Q5_K_M3.1 GB4.9 GB5 GBclose; small differences in fine detailunsloth/FLUX.2-klein-4B-GGUF →
Q4_K_M2.6 GB4.4 GB5 GBgood; some loss in fine detail and textunsloth/FLUX.2-klein-4B-GGUF →
Q3_K_M2.1 GB3.9 GB4 GBnoticeable loss of detailunsloth/FLUX.2-klein-4B-GGUF →
Q2_K1.8 GB3.6 GB4 GBheavy loss; a last resortunsloth/FLUX.2-klein-4B-GGUF →

“Needed” = file + 1 GB working memory + 0.8 GB system reserve.

03Best GPU for FLUX.2 klein 4B

The cheapest cards (by launch price) that run it well, and every card sorted by memory: best GPU for FLUX.2 klein 4B → Planning bigger images or longer clips? Open the calculator →

04By graphics card

GPUVRAMVerdictBest fileNeeded
Desktop graphics cards
RTX 2060 6 GB6 GBRuns wellFP85.9 GB
RTX 3050 6 GB6 GBRuns wellFP85.9 GB
RTX 2070 Super 8 GB8 GBRuns wellQ8_06.1 GB
RTX 2080 Super 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3050 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3060 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3060 Ti 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3070 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3070 Ti 8 GB8 GBRuns wellQ8_06.1 GB
RTX 4060 8 GB8 GBRuns wellFP85.9 GB
RTX 4060 Ti 8 GB8 GBRuns wellFP85.9 GB
RTX 5050 8 GB8 GBRuns wellFP85.9 GB
RTX 5060 8 GB8 GBRuns wellFP85.9 GB
RTX 5060 Ti 8 GB8 GBRuns wellFP85.9 GB
RX 7600 8 GB8 GBRuns wellQ8_06.1 GB
RX 9050 8 GB8 GBRuns wellFP85.9 GB
RX 9060 XT 8 GB8 GBRuns wellFP85.9 GB
Arc B570 10 GB10 GBRuns well16-bit9.6 GB
RTX 3080 10 GB10 GBRuns well16-bit9.6 GB
RTX 2080 Ti 11 GB11 GBRuns well16-bit9.6 GB
Arc B580 12 GB12 GBRuns well16-bit9.6 GB
RTX 2060 12 GB12 GBRuns well16-bit9.6 GB
RTX 3060 12 GB12 GBRuns well16-bit9.6 GB
RTX 3080 12 GB12 GBRuns well16-bit9.6 GB
RTX 3080 Ti 12 GB12 GBRuns well16-bit9.6 GB
RTX 4070 12 GB12 GBRuns well16-bit9.6 GB
RTX 4070 Super 12 GB12 GBRuns well16-bit9.6 GB
RTX 4070 Ti 12 GB12 GBRuns well16-bit9.6 GB
RTX 5070 12 GB12 GBRuns well16-bit9.6 GB
RX 7700 XT 12 GB12 GBRuns well16-bit9.6 GB
RX 9070 GRE 12 GB12 GBRuns well16-bit9.6 GB
Arc A770 16 GB16 GBRuns well16-bit9.6 GB
RTX 4060 Ti 16 GB16 GBRuns well16-bit9.6 GB
RTX 4070 Ti Super 16 GB16 GBRuns well16-bit9.6 GB
RTX 4080 16 GB16 GBRuns well16-bit9.6 GB
RTX 4080 Super 16 GB16 GBRuns well16-bit9.6 GB
RTX 5060 Ti 16 GB16 GBRuns well16-bit9.6 GB
RTX 5070 Ti 16 GB16 GBRuns well16-bit9.6 GB
RTX 5080 16 GB16 GBRuns well16-bit9.6 GB
RX 7600 XT 16 GB16 GBRuns well16-bit9.6 GB
RX 7800 XT 16 GB16 GBRuns well16-bit9.6 GB
RX 7900 GRE 16 GB16 GBRuns well16-bit9.6 GB
RX 9060 XT 16 GB16 GBRuns well16-bit9.6 GB
RX 9070 16 GB16 GBRuns well16-bit9.6 GB
RX 9070 XT 16 GB16 GBRuns well16-bit9.6 GB
RX 7900 XT 20 GB20 GBRuns well16-bit9.6 GB
Arc Pro B60 24 GB24 GBRuns well16-bit9.6 GB
RTX 3090 24 GB24 GBRuns well16-bit9.6 GB
RTX 3090 Ti 24 GB24 GBRuns well16-bit9.6 GB
RTX 4090 24 GB24 GBRuns well16-bit9.6 GB
RX 7900 XTX 24 GB24 GBRuns well16-bit9.6 GB
Arc Pro B70 32 GB32 GBRuns well16-bit9.6 GB
RTX 5090 32 GB32 GBRuns well16-bit9.6 GB
Laptop GPUs
RTX 3050 Laptop 4 GB4 GBTightQ3_K_M3.9 GB
RTX 3050 Ti Laptop 4 GB4 GBTightQ3_K_M3.9 GB
RTX 2060 Laptop 6 GB6 GBRuns wellFP85.9 GB
RTX 3050 Laptop 6 GB6 GBRuns wellFP85.9 GB
RTX 3060 Laptop 6 GB6 GBRuns wellFP85.9 GB
RTX 4050 Laptop 6 GB6 GBRuns wellFP85.9 GB
RTX 2070 Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 2070 Super Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 2080 Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 2080 Super Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3070 Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3070 Ti Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 3080 Laptop 8 GB8 GBRuns wellQ8_06.1 GB
RTX 4060 Laptop 8 GB8 GBRuns wellFP85.9 GB
RTX 4070 Laptop 8 GB8 GBRuns wellFP85.9 GB
RTX 5050 Laptop 8 GB8 GBRuns wellFP85.9 GB
RTX 5060 Laptop 8 GB8 GBRuns wellFP85.9 GB
RTX 5070 Laptop 8 GB8 GBRuns wellFP85.9 GB
RX 7600M 8 GB8 GBRuns wellQ8_06.1 GB
RX 7600M XT 8 GB8 GBRuns wellQ8_06.1 GB
RX 7600S 8 GB8 GBRuns wellQ8_06.1 GB
RX 7700S 8 GB8 GBRuns wellQ8_06.1 GB
RTX 4080 Laptop 12 GB12 GBRuns well16-bit9.6 GB
RTX 5070 Laptop 12 GB12 GBRuns well16-bit9.6 GB
RTX 5070 Ti Laptop 12 GB12 GBRuns well16-bit9.6 GB
RX 7800M 12 GB12 GBRuns well16-bit9.6 GB
RTX 3080 Laptop 16 GB16 GBRuns well16-bit9.6 GB
RTX 3080 Ti Laptop 16 GB16 GBRuns well16-bit9.6 GB
RTX 4090 Laptop 16 GB16 GBRuns well16-bit9.6 GB
RTX 5080 Laptop 16 GB16 GBRuns well16-bit9.6 GB
RX 7900M 16 GB16 GBRuns well16-bit9.6 GB
RTX 5090 Laptop 24 GB24 GBRuns well16-bit9.6 GB
Unified memory
Radeon 8060S (Strix Halo) 96 GB96 GBRuns well16-bit9.6 GB
Apple Silicon Macs (by memory)
Mac 16 GB12.7 GBRuns well16-bit9.6 GB
Mac 18 GB14.4 GBRuns well16-bit9.6 GB
Mac 24 GB19.6 GBRuns well16-bit9.6 GB
Mac 32 GB26.8 GBRuns well16-bit9.6 GB
Mac 36 GB30.2 GBRuns well16-bit9.6 GB
Mac 48 GB40.2 GBRuns well16-bit9.6 GB
Mac 64 GB55.7 GBRuns well16-bit9.6 GB
Mac 96 GB85 GBRuns well16-bit9.6 GB
Mac 128 GB115.4 GBRuns well16-bit9.6 GB
Mac 192 GB175.4 GBRuns well16-bit9.6 GB
Mac 256 GB236.9 GBRuns well16-bit9.6 GB
Mac 512 GB498.1 GBRuns well16-bit9.6 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 4B FP8
qwen_3_4b_fp8_mixed.safetensors
models/text_encoders5.6 GBtext encoder · smaller, recommendedDownload →
Qwen3 4B BF16
qwen_3_4b.safetensors
models/text_encoders8.0 GBtext encoder · alternativeDownload →
FLUX.2 VAE
flux2-vae.safetensors
models/vae0.3 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 4B: 8.0 GB as 16-bit, 5.6 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

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
FP8Native FP8Native FP8No FP8 speed-upNative FP8No FP8 speed-upNo FP8 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 4070 Super 12 GBflux-2-klein-4b (distilled) + qwen_3_4b
“5 秒でした。”
ComfyUI 4B T2I template (distilled, 4 steps default); resolution not stated; base 4B T2I 37 s incl. model load; distilled edit 14-17 s
5 s / image—2026-01-20note.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 FLUX.2 klein 4B

TrainerVRAMTypeSettings and quoteSource
SimpleTuner8 GBstated minimumint8 base (~12 GB in bf16); 32GB+ system RAM; single 16GB GPU recommended
“klein-4b: ~12GB: ~8GB: 32GB+”
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 FLUX.2 klein 4B

FileTypeSizeRepo
flux-2-klein-4b.safetensorsBF167.75 GBblack-forest-labs/FLUX.2-klein-4B →
flux-2-klein-4b-fp8.safetensorsFP84.07 GBblack-forest-labs/FLUX.2-klein-4b-fp8 →
flux-2-klein-4b-BF16.ggufBF167.75 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-F16.ggufF167.75 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q8_0.ggufQ8_04.30 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q6_K.ggufQ6_K3.41 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q5_1.ggufQ5_13.15 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q5_K_M.ggufQ5_K_M3.07 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q5_K_S.ggufQ5_K_S3.05 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q5_0.ggufQ5_02.92 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q4_1.ggufQ4_12.69 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q4_K_M.ggufQ4_K_M2.60 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q4_K_S.ggufQ4_K_S2.58 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q4_0.ggufQ4_02.46 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q3_K_M.ggufQ3_K_M2.12 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q3_K_S.ggufQ3_K_S2.10 GBunsloth/FLUX.2-klein-4B-GGUF →
flux-2-klein-4b-Q2_K.ggufQ2_K1.83 GBunsloth/FLUX.2-klein-4B-GGUF →

EXTRA (2026). Apache-2.0 licensed small FLUX.2. Uses Qwen3-4B text encoder (same file family as Z-Image; see qwen3-4b entry).