Can RTX 3090 Run Flux.1?

Can RTX 3090 Run Flux.1?

Yes, the RTX 3090 can run Flux.1 Dev and Flux.1 Schnell at FP16 with room to spare. The RTX 3090 has 24 GB of VRAM, and Flux.1 requires approximately 12-15 GB for the main transformer model during generation. This makes the 3090 one of the best value GPUs for Flux.1 on a dedicated GPU server.

Flux.1 from Black Forest Labs represents a significant leap over SDXL in prompt adherence, text rendering, and image quality. It is a 12 billion parameter flow-matching transformer model, substantially larger than SDXL’s 3.5B. The 3090’s 24 GB handles it well.

VRAM Analysis: Flux.1 on 24 GB

At FP16, all components loaded simultaneously use about 20 GB, leaving 4 GB headroom on the 3090. With sequential offloading (loading text encoders and transformer separately), peak usage drops to ~15 GB. For the full picture across all SD variants, see our Stable Diffusion VRAM requirements guide.

Generation Speed Benchmarks

Generation times for Flux.1 on the RTX 3090:

Flux.1 Schnell is the distilled variant designed for fast generation (4 steps), producing images in under 5 seconds. Flux.1 Dev needs more steps but produces higher quality output. For GPU speed comparisons, see our best GPU for Stable Diffusion/Flux guide.

Flux.1 Dev vs Schnell vs Pro

Schnell is the only variant with an open license for commercial use. Dev is open-weights but restricted to non-commercial applications. Pro is API-only. For self-hosted production image generation, Schnell is the practical choice. See our image generator hosting page for deployment options.

What Can You Actually Generate?

• 1024×1024 images: Works perfectly in FP16. This is Flux.1’s native resolution.
• 1536×1536 images: Works in FP16 but takes ~40 seconds. Better with FP8 to free VRAM.
• 2048×2048 images: Tight in FP16. Use FP8 or NF4 quantization.
• Batch of 2 at 1024×1024: Possible in FP8 but not in FP16.
• LoRA fine-tuned Flux: Works fine. LoRAs add minimal overhead.
• ControlNet + Flux: Works in FP8 or with model offloading.

The RTX 3090 handles Flux.1 well for single-image workflows. For high-throughput production (many images per minute), consider our multi-GPU options. For comparison, see our RTX 4060 SDXL analysis for what lower-VRAM GPUs can do.

Setup Guide (ComfyUI + Diffusers)

ComfyUI (Recommended)

# Clone and set up ComfyUI
git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
pip install -r requirements.txt

# Download Flux.1 Dev to models/diffusion_models/
# Download T5 encoder to models/text_encoders/
# Download CLIP to models/text_encoders/
# Download VAE to models/vae/

python main.py --force-fp16

Diffusers (Python API)

# Generate with Flux.1 Schnell
pip install diffusers transformers torch
python3 -c "
from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    'black-forest-labs/FLUX.1-schnell',
    torch_dtype=torch.float16
).to('cuda')

image = pipe('A cat wearing sunglasses', num_inference_steps=4).images[0]
image.save('output.png')
"

For full deployment instructions, see our deploy image generation server tutorial and Stable Diffusion/Flux hosting page.

Flux.1 vs SDXL on RTX 3090

Flux.1 produces better images but uses more VRAM and is slower per image (except Schnell). SDXL gives you more headroom for batching on the 3090. For cost analysis, check our cheapest GPU for AI inference guide and the GPU comparison tool.