Can RTX 3090 Ti run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~42 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

24 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 42 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 3090 Ti

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

TL;DR

FLUX.1 Schnell (Q5_0) runs at Grade S on an NVIDIA GeForce RTX 3090 Ti, delivering ~64 tok/sec with snappy performance.

Prerequisites

Before starting, ensure you have at least 12.0GB of free disk space, a compatible OS (Windows 10/11 or Linux), the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 installed.

Expected performance

You can expect ~64 tok/sec with the Q5_0 quantization, using 14.0GB of VRAM. The remaining 10.0GB of VRAM provides ample headroom for a large context window, enabling state-of-the-art quality in fast 1-4 step generation.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the 12.0GB Q5_0 quantized model from the Hugging Face repository.

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

3. Run it

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

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster inference. With 14.0GB VRAM used by the model, you have 10.0GB of headroom for context, allowing for a practical context window of several thousand tokens.

Troubleshooting

Out of memory error during inference

Reduce --n-gpu-layers to 24 or enable --cpu-offload to offload some layers to the CPU.

Low token generation speed

Ensure --flash-attn is enabled and check that your CUDA installation is up-to-date.

Model fails to load

Verify the model file integrity and try re-downloading it using the 'ollama pull' command.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a GUI for easier management, llama.cpp provides more control over quantization, and Jan is lightweight for embedded systems. However, Ollama is recommended for its ease of use and robust performance on the NVIDIA GeForce RTX 3090 Ti.

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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