Can RTX 5090 run FLUX.1 Schnell (GGUF)?

Yes — runs locally

~78 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

32 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 5090 (32 GB VRAM) handles FLUX.1 Schnell (GGUF) comfortably using the Q5_0 quantization, which fits in 14.0 GB. Expected throughput is around 78 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 5090

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

TL;DR

The FLUX.1 Schnell (GGUF) model runs at Grade S on an NVIDIA GeForce RTX 5090 with Q5_0 quantization, achieving ~85 tok/sec.

Prerequisites

Before starting, ensure you have at least 12GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) installed along with CUDA 11.8.

Expected performance

With the recommended settings, you can expect the FLUX.1 Schnell (GGUF) model to run at approximately 85 tokens per second, utilizing 14.0GB of VRAM. Given the remaining 18.0GB of VRAM, you can achieve a practical context window of several thousand tokens, depending on the specific requirements of your task.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the 12.0GB Q5_0 quantized model from Hugging Face.

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

3. Run it

ollama run gpustack/FLUX.1-schnell-GGUF:Q5_0 --interactive
ollama stream

4. Optimize for RTX 5090

For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU, enable flash attention with --flash-attn, and consider using tensor parallelism with --tensor-parallel-size 2. This configuration will help achieve the target ~85 tok/sec while keeping VRAM usage around 14.0GB, leaving 18.0GB of headroom for larger context windows.

Troubleshooting

Out of memory errors during inference

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

Low token generation speed

Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is correctly configured.

Inconsistent performance across runs

Check for background processes consuming GPU resources and close them. Also, ensure that the GPU drivers and CUDA are up to date.

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more graphical interface, llama.cpp for advanced customization options, or Jan for lightweight deployment. Each has its own strengths, but Ollama is generally recommended for its ease of use and performance on the NVIDIA GeForce RTX 5090.

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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