Developer Runs FLUX AI Image Model Locally on an RTX 4070 Using Quantization

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A developer has shared a workflow for running the FLUX image generation model locally on a consumer-grade RTX 4070 GPU, a card with 12GB of VRAM that would normally struggle with the model's 24GB requirements. The approach relies on 4-bit NF4 quantization via the bitsandbytes library to compress model weights, combined with LoRA fine-tuning to train on custom artwork without modifying the base model. Additional techniques such as embedding caching, lower training resolution, and gradient checkpointing help keep memory usage within the card's limits. The developer trained the quantized model on 14 personal artworks paired with a metadata file to generate images in their own artistic style. The method, originally demonstrated by YouTuber Richard Aragon via a Google Colab notebook, is also reported to work on the less powerful RTX 4060.

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