Can RTX 4070 SUPER run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~26 tok/sec · Good — slight pause, then text streams smoothly.

Your VRAM

12 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 4070 SUPER (12 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 26 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Fast 1-4 step generation. State-of-the-art quality. Needs 16GB+ RAM.

Setup tutorial: FLUX.1 Schnell (GGUF) on RTX 4070 SUPER

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

FLUX.1 Schnell (Q5_0) runs on an NVIDIA GeForce RTX 4070 SUPER with a Grade C performance, achieving ~32 tokens/sec. It requires 14.0GB VRAM, leaving 2.0GB for context.

Prerequisites

Before starting, ensure you have at least 12.0GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.85.12 or later), and CUDA 11.8 installed.

Expected performance

With the recommended settings, you can expect the model to run at ~32 tokens/sec, using 14.0GB of VRAM. Given the 2.0GB headroom, you can achieve a practical context window of around 2048 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_0 quantized version of FLUX.1 Schnell, which is a 12.0GB file from the Hugging Face repository.

ollama pull gpustack/FLUX.1-schnell-GGUF:FLUX.1-schnell-Q5_0.gguf

3. Run it

ollama run FLUX.1-schnell-Q5_0 --n-gpu-layers 32 --flash-attn --tensor-parallelism 1

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 32 to utilize most of the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 1 to avoid splitting the model across multiple GPUs. This configuration will use approximately 14.0GB VRAM, leaving 2.0GB for context.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.

Slow token generation speed

Ensure CUDA is properly installed and enabled in Ollama. Also, check if the latest NVIDIA drivers are installed.

Inconsistent performance

Disable any background processes that may be using the GPU, and ensure the system is not overheating.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for users who prefer a graphical interface and need more control over the model's parameters. llama.cpp is ideal for those who want a lightweight, command-line tool with advanced customization options. Jan is a good choice for users looking for a simple, easy-to-use runtime with minimal setup. For the NVIDIA GeForce RTX 4070 SUPER, Ollama provides a balanced approach with good performance and ease of use.

Full FLUX.1 Schnell (GGUF) details →

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FAQ (20)

What GPU do I need to run FLUX.1 Schnell (GGUF)?

To run FLUX.1 Schnell (GGUF), you need a GPU with at least 14 GB of VRAM. NVIDIA RTX 3090 or higher is recommended.

Is FLUX.1 Schnell (GGUF) good for coding?

FLUX.1 Schnell (GGUF) is primarily designed for image generation and may not be optimized for coding tasks. Consider other models specifically designed for code generation.

FLUX.1 Schnell (GGUF) vs Llama 3.1 8B?

FLUX.1 Schnell (GGUF) has 12B parameters and focuses on fast image generation, while Llama 3.1 8B is smaller and more versatile, suitable for a wider range of tasks including text generation.

Can I run FLUX.1 Schnell (GGUF) on a Mac?

Yes, you can run FLUX.1 Schnell (GGUF) on a Mac with an M1 or M2 chip, provided you have at least 16GB of RAM and the necessary drivers for GPU acceleration.

How much VRAM does FLUX.1 Schnell (GGUF) need?

FLUX.1 Schnell (GGUF) requires 14 GB of VRAM to run efficiently, regardless of quantization level.

Is FLUX.1 Schnell (GGUF) censored?

FLUX.1 Schnell (GGUF) is not explicitly censored, but it adheres to community guidelines and ethical standards set by Black Forest Labs.

Is FLUX.1 Schnell (GGUF) commercial-use allowed?

Yes, FLUX.1 Schnell (GGUF) is licensed under Apache-2.0, which allows for commercial use as long as you comply with the terms of the license.

FLUX.1 Schnell (GGUF) context length?

The context length for FLUX.1 Schnell (GGUF) is currently unknown, but it is optimized for fast 1-4 step image generation.

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