The NVIDIA Jetson Orin Nano 8GB is equipped with 8GB of LPDDR5 VRAM and an Ampere architecture GPU, including 1024 CUDA cores and 32 Tensor cores. FLUX.1 Schnell, a diffusion model, requires 24GB of VRAM in FP16 precision due to its 12 billion parameters. The Orin Nano's 8GB VRAM falls significantly short, resulting in a VRAM deficit of 16GB. This incompatibility prevents the model from loading entirely onto the GPU, making direct inference impossible without significant optimization or alternative approaches.
Memory bandwidth, at 0.07 TB/s, is also a limiting factor. Even if the model could be squeezed into the available VRAM through quantization, the relatively low memory bandwidth would severely bottleneck the model's performance, leading to extremely slow token generation. The combination of insufficient VRAM and limited memory bandwidth means that real-time or even practical inference speeds are unachievable with this configuration.
Due to the substantial VRAM deficit, running FLUX.1 Schnell directly on the Jetson Orin Nano 8GB is not feasible without extreme quantization or offloading techniques. Consider using a more powerful GPU with at least 24GB of VRAM for optimal performance. Alternatively, explore techniques like model parallelism or offloading layers to system RAM, though this will significantly degrade performance. For the Orin Nano, focus on smaller models that fit within its VRAM capacity or utilize cloud-based inference services.
If you are determined to run FLUX.1 Schnell on the Orin Nano, investigate aggressive quantization methods (e.g., 4-bit or even 2-bit) combined with CPU offloading. However, expect a dramatic reduction in image quality and generation speed. A more practical approach might involve using the Orin Nano for pre-processing or post-processing tasks within a larger AI pipeline, delegating the actual diffusion modeling to a more capable device.