The NVIDIA Jetson AGX Orin 32GB is well-suited for running the FLUX.1 Schnell diffusion model. With 32GB of LPDDR5 VRAM, the Orin comfortably exceeds the model's 24GB FP16 VRAM requirement, leaving a substantial 8GB headroom. This is crucial because diffusion models often require additional VRAM for intermediate calculations and larger batch sizes. The Ampere architecture, with its 1792 CUDA cores and 56 Tensor Cores, provides ample computational power for accelerating the model's forward pass. While the memory bandwidth of 210 GB/s is a limiting factor compared to desktop GPUs, it's sufficient for achieving reasonable inference speeds on the Orin.
To maximize performance on the Jetson AGX Orin, leverage TensorRT for model optimization and quantization. Experiment with INT8 quantization to reduce memory footprint and improve inference speed, though be mindful of potential accuracy trade-offs. Start with a batch size of 3, as indicated, and monitor VRAM usage closely. If possible, try increasing the batch size further to improve throughput, but avoid exceeding the available VRAM. Due to the limited context length of 77 tokens, consider using techniques like sliding window attention or truncation to handle longer sequences if necessary.