Can RTX 4080 SUPER run FLUX.1 Schnell (GGUF)?
Yes — runs locally
~46 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
The FLUX.1 Schnell (GGUF) model runs well on an NVIDIA GeForce RTX 4080 SUPER with a grade B performance, using the Q5_0 quantization, achieving ~42 tok/sec.
Prerequisites
Before starting, ensure you have at least 16GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.7 or later installed.
Expected performance
You can expect the model to run at ~42 tok/sec with 14.0GB VRAM in use, leaving 2.0GB of headroom for context. This should allow for a practical context window of several hundred tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. 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_03. Run it
ollama run FLUX.1-schnell-Q5_0 --n-gpu-layers 32 --flash-attn
ollama chat --model FLUX.1-schnell-Q5_04. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn to speed up attention calculations. With 14.0GB VRAM used by the model, you have 2.0GB of headroom for context, which should support a reasonable context window.
Troubleshooting
Out of memory errors during inference
Reduce --n-gpu-layers to 24 or 16 to lower VRAM usage.
Slow token generation
Ensure --flash-attn is enabled to optimize attention calculations.
Inconsistent performance
Check for background processes consuming GPU resources and close them.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface and is suitable for users who prefer a GUI. llama.cpp is ideal for advanced users who need fine-grained control over model execution. Jan is lightweight and efficient, making it a good choice for systems with limited resources. For the NVIDIA GeForce RTX 4080 SUPER, Ollama provides a balanced combination of ease of use and performance.
Other models that run great on RTX 4080 SUPER
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 →