Can RTX 5070 Ti run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~46 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 5070 Ti (16 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 46 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 5070 Ti

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

TL;DR

FLUX.1 Schnell (Q5_0) runs on the NVIDIA GeForce RTX 5070 Ti with a Grade B performance, achieving ~42 tok/sec. It requires 14.0GB VRAM and 12.0GB disk space.

Prerequisites

Before starting, ensure you have at least 12.0GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed. You also need CUDA 11.8 or later.

Expected performance

With the recommended settings, you can expect ~42 tok/sec performance and 14.0GB VRAM usage, leaving 2.0GB for context. Given the remaining VRAM, you can achieve a practical context window of around 2048 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the 12.0GB Q5_0 quantized model from Hugging Face.

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

3. Run it

ollama run FLUX.1-schnell-Q5_0.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 5070 Ti

For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 32 to utilize most of the VRAM. Enable --flash-attn for faster attention calculations and set --tensor-parallelism to 2 to distribute the workload efficiently. This configuration will allow you to achieve ~42 tok/sec while using 14.0GB of 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 --flash-attn is enabled and --tensor-parallelism is set to 2.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.

Alternative runtimes

If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for low-level control, or Jan for specialized use cases. For the NVIDIA GeForce RTX 5070 Ti, Ollama is generally the best choice due to its ease of use and performance optimizations.

Full FLUX.1 Schnell (GGUF) details →

Other models that run great on RTX 5070 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →