The NVIDIA Jetson Orin Nano 8GB is exceptionally well-suited for running the BGE-M3 embedding model. The Orin Nano's 8GB of LPDDR5 VRAM provides ample headroom for the model's 1.0GB FP16 VRAM requirement, leaving a substantial 7GB buffer for other processes and larger batch sizes. While the memory bandwidth of 0.07 TB/s might be a limiting factor for larger models, it is more than sufficient for the relatively small 0.5B parameter BGE-M3 model. The Ampere architecture, with its 1024 CUDA cores and 32 Tensor Cores, provides a strong foundation for efficient matrix operations crucial for embedding generation.
Given the generous VRAM headroom, users can experiment with larger batch sizes (up to 32) to maximize throughput. Consider using a framework like ONNX Runtime or TensorRT to further optimize inference speed on the Jetson Orin Nano. While FP16 precision should work well, exploring INT8 quantization could provide an additional performance boost with minimal accuracy loss, especially if memory bandwidth becomes a bottleneck at higher batch sizes. Monitoring GPU utilization and memory usage is crucial to fine-tune the optimal batch size and context length for your specific application.