Yes, but only with significant compromises. The RTX 4060 can run Flux.1 Schnell in FP8 or NF4 quantisation using ComfyUI, but its 8GB VRAM makes the full Flux.1 Dev model extremely tight. If you are evaluating RTX 4060 hosting for Flux image generation, expect slower speeds and lower resolution limits compared to cards with more VRAM.
The Short Answer
YES for Flux.1 Schnell in FP8/NF4. Barely for Flux.1 Dev with aggressive optimisation.
Flux.1 is a 12B parameter diffusion transformer model from Black Forest Labs. In FP16, the model weights alone consume approximately 24GB of VRAM, which rules out the RTX 4060 entirely at full precision. However, FP8 quantisation brings this down to roughly 12GB, and NF4 quantisation reduces it further to about 7GB. With NF4 and memory-efficient attention, you can generate 512×512 images on the 8GB RTX 4060.
Flux.1 Schnell (the distilled, faster variant) requires fewer inference steps (4 vs 20-50 for Dev), which reduces peak VRAM usage during generation. This is the practical option for 8GB cards.
VRAM Analysis
| Configuration | Model VRAM | Generation Overhead | Total (512×512) | RTX 4060 (8GB) |
|---|---|---|---|---|
| Flux.1 Dev FP16 | ~24GB | ~2GB | ~26GB | No |
| Flux.1 Dev FP8 | ~12GB | ~1.5GB | ~13.5GB | No |
| Flux.1 Dev NF4 | ~7GB | ~1.5GB | ~8.5GB | Borderline |
| Flux.1 Schnell FP8 | ~12GB | ~1GB | ~13GB | No |
| Flux.1 Schnell NF4 | ~7GB | ~0.8GB | ~7.8GB | Tight fit |
With NF4 quantisation and ComfyUI’s memory management (which offloads components to system RAM as needed), Flux.1 Schnell can fit within 8GB for 512×512 generation. Going to 1024×1024 increases the latent tensor size and will push you into OOM territory. For full details, see our Flux.1 VRAM requirements analysis.
Performance Benchmarks
| GPU | Model | Quant | Resolution | Time per Image |
|---|---|---|---|---|
| RTX 4060 (8GB) | Schnell NF4 | NF4 | 512×512 | ~12s (4 steps) |
| RTX 4060 Ti (16GB) | Schnell FP8 | FP8 | 1024×1024 | ~18s (4 steps) |
| RTX 3090 (24GB) | Dev FP16 | FP16 | 1024×1024 | ~25s (20 steps) |
| RTX 5080 (16GB) | Schnell FP8 | FP8 | 1024×1024 | ~10s (4 steps) |
At 12 seconds per 512×512 image with Schnell, the RTX 4060 is functional for personal use but not for batch generation. The quality at NF4 quantisation is noticeably softer than FP16, particularly in fine details and text rendering. Check our benchmarks page for throughput comparisons across all cards.
Setup Guide
ComfyUI is the recommended interface for running Flux.1 on VRAM-constrained cards:
# Clone ComfyUI
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
# Launch with aggressive memory management
python main.py --lowvram --listen 0.0.0.0 --port 8188Download the Flux.1 Schnell NF4 checkpoint from HuggingFace and place it in models/unet/. Use the GGUF loader nodes in ComfyUI for NF4 models. The --lowvram flag ensures components are offloaded to CPU RAM when not actively in use, which is essential for staying within 8GB.
Avoid loading the text encoders (T5-XXL and CLIP-L) simultaneously with the UNet. ComfyUI’s sequential loading handles this automatically with the lowvram flag.
Recommended Alternative
For Flux.1 at full quality, the RTX 4060 Ti with 16GB runs Schnell in FP8 at 1024×1024 without memory tricks. The RTX 3090 with 24GB handles Flux.1 Dev in FP16 at full resolution, which is the quality benchmark.
If you are interested in newer GPUs, check whether the RTX 5080 can run Flux.1 for a significant speed boost. For LLM workloads on the same card, see our RTX 4060 DeepSeek analysis or the RTX 4060 Whisper guide. Our best GPU for image generation guide covers all options, and dedicated GPU servers let you pick the right card for your needs.
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