Can RTX 3080 Ti run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

12 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — model too large for this GPU in interactive use. Fast 1-4 step generation. State-of-the-art quality. Needs 16GB+ RAM.

Setup tutorial: FLUX.1 Schnell (GGUF) on RTX 3080 Ti

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

TL;DR

The FLUX.1 Schnell (GGUF) model runs on an NVIDIA GeForce RTX 3080 Ti with a Grade C performance, using the Q5_0 quantization, achieving ~32 tokens per second.

Prerequisites

Before starting, ensure you have at least 16GB of system RAM, 12GB of free disk space, and are running a supported OS (Windows 10/11 or Linux). Install the latest NVIDIA drivers (version 470.82.01 or later) and CUDA 11.4 or later.

Expected performance

With the Q5_0 quantization, you can expect the model to run at approximately 32 tokens per second, utilizing about 14.0GB of VRAM. Given the 12GB VRAM limit, you will have a headroom of -2.0GB for context, which means you should aim for a practical context window of around 1024 tokens to avoid out-of-memory errors.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

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

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

3. Run it

ollama run FLUX.1-schnell-Q5_0 --n-gpu-layers 12 --flash-attn
ollama interactive FLUX.1-schnell-Q5_0

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, set --n-gpu-layers to 12 to maximize the number of layers offloaded to the GPU. Enable --flash-attn to reduce memory usage and improve speed. Due to the 12GB VRAM limit, you may need to adjust the context window to fit within the available memory, typically around 1024 tokens.

Troubleshooting

Out of memory error during inference

Reduce the context window size or decrease the --n-gpu-layers value to 8 or 6.

Slow token generation rate

Ensure that --flash-attn is enabled and try increasing the batch size if your application supports it.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the 'ollama pull' command.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp is highly optimized for low-memory systems and can be a good choice if you need to push the limits of your GPU's VRAM. Jan is another lightweight runtime that can be used for quick prototyping and testing.

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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