The primary limiting factor in running the FLUX.1 Dev model on an NVIDIA RTX 4070 is the VRAM capacity. FLUX.1 Dev, with its 12 billion parameters, requires approximately 24GB of VRAM when using FP16 (half-precision floating point) data types for the model weights. The RTX 4070, however, only provides 12GB of VRAM. This 12GB deficit means the model cannot be loaded and processed in its entirety on the GPU, leading to a compatibility failure. While the RTX 4070's memory bandwidth of 0.5 TB/s and its Ada Lovelace architecture are beneficial for AI tasks, they cannot overcome the fundamental limitation of insufficient VRAM.
Even if techniques like offloading layers to system RAM were employed, the performance would be severely degraded due to the much slower transfer speeds between system RAM and the GPU compared to VRAM. The model's context length of 77 tokens is relatively small, minimizing the impact of context size on VRAM usage; however, this does not alleviate the core issue of the model's overall memory footprint exceeding the GPU's capacity. The CUDA cores and Tensor cores would be underutilized because the model cannot fit entirely on the GPU.
Due to the VRAM limitations, running FLUX.1 Dev on an RTX 4070 in its original FP16 format is not feasible. To potentially run this model, aggressive quantization techniques, such as using 4-bit or 8-bit quantization, are necessary to significantly reduce the model's memory footprint. Consider using inference frameworks like llama.cpp which are optimized for CPU+GPU inference and offer extensive quantization support. Alternatively, explore using cloud-based GPU instances with sufficient VRAM (e.g., an A100 or H100 instance) if local execution is not mandatory.
If you choose to pursue local execution with quantization, be prepared for a potential reduction in model accuracy and performance. Experiment with different quantization levels to find a balance between memory usage and output quality. Monitor GPU utilization and memory usage carefully to identify potential bottlenecks. If even with quantization, the model still exceeds the available VRAM, consider using a smaller model variant or exploring alternative diffusion models with lower memory requirements.