The NVIDIA Jetson AGX Orin 64GB, with its Ampere architecture, 2048 CUDA cores, and 64 Tensor cores, is well-suited for running the FLUX.1 Dev diffusion model. A crucial aspect of compatibility is VRAM. FLUX.1 Dev, at 12.0B parameters, requires 24.0GB of VRAM when using FP16 precision. The Jetson AGX Orin's 64GB LPDDR5 memory provides ample headroom (40GB), ensuring the model fits entirely within the GPU's memory. This avoids performance bottlenecks associated with swapping data between system RAM and VRAM.
Memory bandwidth is also a key factor. The Jetson AGX Orin's 210 GB/s memory bandwidth allows for relatively fast data transfer between the GPU cores and the memory. Given the model size and available bandwidth, we estimate a throughput of approximately 72 tokens per second. This estimate factors in the computational intensity of diffusion models and the Jetson AGX Orin's processing capabilities. Batch size significantly impacts performance; a larger batch size generally increases throughput but also increases VRAM usage.
Finally, the Ampere architecture's Tensor Cores accelerate matrix multiplication operations, which are fundamental to deep learning. This hardware acceleration contributes to the overall performance of the FLUX.1 Dev model on the Jetson AGX Orin.
The NVIDIA Jetson AGX Orin 64GB is an excellent platform for running FLUX.1 Dev. To maximize performance, start with a batch size of 16 and monitor VRAM usage. If you have other applications running on the Jetson, you might need to reduce the batch size to avoid memory exhaustion. Experiment with different inference frameworks like TensorRT or ONNX Runtime to further optimize performance. These frameworks can provide graph optimizations and kernel fusion, potentially increasing tokens per second.
Consider using quantization techniques like INT8 or even INT4 to reduce VRAM footprint and potentially increase inference speed. While FP16 offers good precision, for diffusion models, a slight reduction in precision might not significantly impact the quality of generated images. Always test the generated output after quantization to ensure the quality remains acceptable. Furthermore, profile the model's execution to identify any bottlenecks and optimize accordingly.