Can RTX 4060 Ti 16GB run FLUX.1 Schnell (GGUF)?

Yes — runs locally

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

Your VRAM

16 GB

Model size

12B

Best quant

Q5_0

VRAM needed

14.0 GB

The verdict

The RTX 4060 Ti 16GB (16 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 4060 Ti 16GB

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

TL;DR

FLUX.1 Schnell (Q5_0) runs on the NVIDIA GeForce RTX 4060 Ti 16GB with a Grade B performance, achieving ~42 tok/sec. It requires 14.0GB VRAM and 12.0GB disk space.

Prerequisites

Before starting, ensure you have at least 12.0GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.85.12 or later), and CUDA 11.7 or later installed.

Expected performance

With the recommended settings, you can expect ~42 tok/sec performance and 14.0GB VRAM usage, leaving 2.0GB for context. This allows for a practical context window of several thousand tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama config set backend cuda

2. Download the model

Download the Q5_0 quantized version of FLUX.1 Schnell, which is a 12.0GB file.

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

3. Run it

ollama run --model FLUX.1-schnell-GGUF:Q5_0 --n-gpu-layers 32 --flash-attn
ollama interactive --model FLUX.1-schnell-GGUF:Q5_0

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use --n-gpu-layers 32 to offload layers to the GPU. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Given the 16GB VRAM, you can allocate up to 14.0GB for the model, leaving 2.0GB for context and other overheads.

Troubleshooting

Out of memory errors during inference

Reduce --n-gpu-layers to 24 or lower and ensure --flash-attn is enabled.

Slow token generation

Ensure CUDA is properly configured and try increasing --n-gpu-layers to 32.

Model fails to load

Verify the model file integrity and ensure the Ollama runtime is up to date with 'pip install --upgrade ollama'.

Alternative runtimes

Alternative runtimes include LM Studio and llama.cpp. LM Studio offers a more user-friendly interface and is suitable for users who prefer a graphical environment. llama.cpp provides more fine-grained control over execution parameters and is ideal for advanced users or those requiring specific optimizations. Jan is another lightweight option but may not support all features of FLUX.1 Schnell.

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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