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

Yes — runs locally

~40 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

16 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 4070 Ti SUPER (16 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 40 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 Ti SUPER

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

TL;DR

FLUX.1 Schnell (GGUF) runs well on an NVIDIA GeForce RTX 4070 Ti SUPER with a grade B performance, using the Q5_0 quantization, achieving ~42 tokens per second.

Prerequisites

Before starting, ensure you have at least 16GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.85.12 or later) installed along with CUDA 11.8 or higher.

Expected performance

You can expect a token generation rate of ~42 tok/sec with 14.0GB VRAM in use, leaving 2.0GB of VRAM for context. This setup should handle a practical context window of around 2048 tokens, suitable for most tasks.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q5_0 quantized version of FLUX.1 Schnell (12.0GB file) from the Hugging Face repository.

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

3. Run it

ollama run --model FLUX.1-schnell-GGUF:Q5_0 --interactive
ollama generate --model FLUX.1-schnell-GGUF:Q5_0 --prompt 'Your prompt here'

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 12 to utilize the GPU efficiently. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 14.0GB VRAM required for the model, you will have approximately 2.0GB of VRAM left for context, allowing for a practical context window of around 2048 tokens.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or decrease the batch size.

Slow token generation

Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features. LM Studio is ideal for a more user-friendly interface, llama.cpp offers fine-grained control over quantization and performance settings, and Jan is suitable for cloud deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 4070 Ti SUPER.

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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