Can RTX 4090 run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~66 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 4090 (24 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 66 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Fast 1-4 step generation. State-of-the-art quality. Needs 16GB+ RAM.

Setup tutorial: FLUX.1 Schnell (GGUF) on RTX 4090

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 4090, achieving ~64 tok/sec with 14.0GB VRAM usage.

Prerequisites

Before starting, ensure you have at least 24GB 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 or later installed.

Expected performance

With the Q5_0 quantization, you should expect ~64 tok/sec with 14.0GB VRAM in use, leaving 10.0GB of headroom for context. This allows for a practical context window of around 2048 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama config set device cuda

2. Download the model

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

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

3. Run it

ollama run FLUX.1-schnell-GGUF:Q5_0 --interactive
ollama config set n_gpu_layers 48

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090, set --n-gpu-layers to 48 to fully utilize the 24GB VRAM. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Tensor parallelism can be set to 2 to further optimize performance, but it may require additional fine-tuning.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 32 or enable --flash-attn to lower VRAM usage.

Slow token generation

Ensure CUDA is properly installed and configured. Set --n-gpu-layers to 48 and enable --flash-attn.

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 include LM Studio, llama.cpp, and Jan. LM Studio is ideal for a more user-friendly interface, while llama.cpp offers more control over low-level optimizations. Jan is suitable for distributed training and inference scenarios. For the NVIDIA GeForce RTX 4090, Ollama provides a good balance of ease of use and performance.

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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