Best GPU for FLUX: VRAM Requirements, Speed, and Cloud Pricing

FLUX.1 Dev needs at least 12GB VRAM and runs best on 24GB+ GPUs. The RTX 4090 (24GB, $0.59/hr on JarvisLabs) is the best value — fast generation with enough VRAM for full-quality output. For batch generation on a budget, the L4 (24GB, $0.44/hr) works well. For production APIs serving FLUX at scale, the H100 delivers the highest throughput.

FLUX Model Variants

FLUX is a family of models from Black Forest Labs. Each variant has different requirements:

FLUX.1 Pro is available only through API and doesn't require your own GPU. FLUX.1 Dev and Schnell can run locally or on cloud GPUs.

VRAM Requirements

FLUX uses a DiT (Diffusion Transformer) architecture that's more memory-hungry than Stable Diffusion's UNet:

Key difference from Stable Diffusion: FLUX's transformer backbone uses more VRAM than SD's UNet. A GPU that runs SDXL comfortably may be tight with FLUX. 24GB is the comfortable minimum for full-quality FLUX Dev.

GPU Recommendations

Best Overall: NVIDIA RTX 4090 (24GB)

The RTX 4090 is the best GPU for FLUX for most users:

• 24GB VRAM — fits FLUX Dev at full FP16 quality with room for ControlNet and LoRAs
• Fast generation — FLUX Schnell in ~2-4 seconds, FLUX Dev in ~15-30 seconds (20 steps)
• 4th-gen Tensor Cores — good acceleration for transformer inference
• $0.59/hr on JarvisLabs — excellent value for interactive image generation

Whether you're using ComfyUI or running FLUX via API, the RTX 4090 handles it without compromises. Check our pricing page.

Best Budget: NVIDIA L4 (24GB)

The L4 runs FLUX at a lower price point:

• 24GB VRAM — same memory as RTX 4090, so FLUX Dev fits at full quality
• Slower generation — roughly 2-3x slower than RTX 4090
• $0.44/hr on JarvisLabs — cheapest 24GB option
• Efficient for batch jobs — lower cost per image for overnight generation

The L4 is the pick when you're generating many images and time-per-image isn't critical.

Best for Production APIs: NVIDIA H100 (80GB)

For serving FLUX to multiple users simultaneously:

• 80GB VRAM — load multiple FLUX variants, ControlNet models, and LoRAs simultaneously
• Highest throughput — serve more concurrent requests per GPU
• FP8 acceleration — run FLUX in FP8 for faster inference with minimal quality impact
• $2.69/hr on JarvisLabs — justified when maximizing throughput

The H100 makes sense for production image generation services where you need maximum images per second and can batch requests efficiently.

Best for FLUX + Extensions: NVIDIA RTX 6000 Ada (48GB) or A6000 (48GB)

If you're running complex FLUX workflows:

• 48GB VRAM — FLUX + ControlNet + IP-Adapter + LoRAs without memory pressure
• Room for upscalers — load FLUX and a 4x upscaler simultaneously
• RTX 6000 Ada at $0.99/hr, A6000 at $0.79/hr — mid-range pricing

For ComfyUI power users running complex multi-model pipelines, 48GB eliminates VRAM management entirely.

For Tight Budgets: Quantized FLUX

If your GPU has less than 24GB VRAM, quantized FLUX variants can help:

• FP8 FLUX — needs ~8-10GB VRAM. Minor quality loss. Works on 12-16GB GPUs
• NF4 FLUX — needs ~6-8GB VRAM. Noticeable quality loss for some prompts. Works on 8GB+ GPUs

Quantization lets you run FLUX on cheaper GPUs but at the cost of image quality and sometimes prompt adherence. For consistent quality, use full FP16 on a 24GB GPU.

GPU Speed Comparison

Approximate speeds at 1024×1024, batch size 1, FP16. Actual performance varies by software stack, sampler, and system configuration.

Cost-per-image analysis: For FLUX Dev (20 steps), the RTX 3090 often delivers the lowest cost per image despite being slower — $0.29/hr × ~30 seconds per image. The RTX 4090 is best for interactive use where generation speed matters.

FLUX vs Stable Diffusion GPU Requirements

FLUX requires roughly 2x the VRAM and generates images slower than SDXL. The tradeoff is significantly better prompt adherence and image quality, especially for text rendering and complex compositions.

If you're currently running SDXL on a 12GB GPU, you'll likely need to upgrade to 24GB for comfortable FLUX usage, or use quantized FLUX variants.

FLUX LoRA Training

Fine-tuning FLUX with LoRA to customize the model for specific styles or subjects:

Training tools: ai-toolkit and SimpleTuner are the most popular FLUX training frameworks. Both support mixed-precision training and gradient checkpointing to reduce VRAM requirements.

Training Cost Estimates

FLUX LoRA training (1,000 steps, rank 16):

The RTX 4090 offers the best training cost for FLUX LoRAs. Check JarvisLabs pricing.

Running FLUX on JarvisLabs

Getting FLUX running on a JarvisLabs instance:

1. Launch an RTX 4090 or A100 instance with a PyTorch template
2. Install ComfyUI or your preferred FLUX frontend
3. Download the FLUX model (FLUX.1 Dev is ~24GB)
4. Start generating

Your workspace persists between sessions — the downloaded model, ComfyUI installation, and custom nodes stay in place when you stop and restart the instance. No need to re-download or re-setup.

For ComfyUI workflows, JarvisLabs also supports serverless GPU endpoints where you can run FLUX as an API without managing instances.

FAQ

What is the minimum VRAM for FLUX?

FLUX Schnell runs on 10GB+. FLUX Dev needs 12GB minimum at FP16. With NF4 quantization, FLUX Dev can run on 8GB GPUs with some quality loss. For reliable, full-quality results, 24GB is recommended.

Is FLUX faster or slower than Stable Diffusion?

Slower. FLUX uses a larger transformer architecture. At the same step count, FLUX takes 3-5x longer than SDXL per image. However, FLUX Schnell produces good results in just 4 steps, making it competitive in total generation time.

Can I run FLUX on an RTX 3090?

Yes. The RTX 3090 has 24GB VRAM, which fits FLUX Dev at FP16. Generation is slower than the RTX 4090 but works well. At $0.29/hr on JarvisLabs, it's the cheapest way to run full-quality FLUX. Check our pricing page.

FLUX Dev or FLUX Schnell — which should I use?

FLUX Schnell for speed (4 steps, good quality). FLUX Dev for maximum quality (20-50 steps, better detail and prompt adherence). GPU requirements are similar — the difference is generation time, not VRAM.

How much does it cost to generate 10,000 images with FLUX?

On JarvisLabs with an RTX 4090 ($0.59/hr) using FLUX Dev at ~20 seconds per image: 10,000 images ≈ 56 hours ≈ $33. With FLUX Schnell at ~3 seconds per image: ~8 hours ≈ $5. With RTX 3090 ($0.29/hr) the cost drops further.

Can I fine-tune FLUX with LoRA?

Yes. FLUX LoRA training needs 16-24GB VRAM minimum. An RTX 4090 handles most LoRA training. For higher-rank LoRAs or DreamBooth training, an A100 80GB gives more headroom. See the training section above for details.

Is FLUX better than Stable Diffusion?

For text rendering, prompt adherence, and photorealistic generation, FLUX generally outperforms SDXL. SDXL has a larger ecosystem of LoRAs, ControlNets, and community tools. Many users run both depending on the task. See our Stable Diffusion GPU guide for SD-specific recommendations.