The NVIDIA Jetson AGX Orin 32GB, with its Ampere architecture, 1792 CUDA cores, and 32GB of LPDDR5 VRAM, provides ample resources for running the BGE-Large-EN embedding model. BGE-Large-EN, a relatively small model at 0.33B parameters, requires approximately 0.7GB of VRAM in FP16 precision. The Orin's 32GB VRAM offers a significant headroom of 31.3GB, ensuring that the model and associated operations can be loaded and executed without memory constraints. This abundant VRAM allows for larger batch sizes and potentially the concurrent execution of other tasks on the device.
Furthermore, the Orin's memory bandwidth of 0.21 TB/s is sufficient for the data transfer demands of BGE-Large-EN. Embedding models, while not as computationally intensive as large language models, still benefit from high memory bandwidth, especially when processing large batches of text. The 56 Tensor Cores contribute to accelerating the matrix multiplications inherent in the embedding process. With the Orin's TDP of 40W, power consumption should be manageable, making it suitable for edge deployment scenarios. The expected performance is around 90 tokens/sec, which is a reasonable speed for embedding tasks on an edge device.
Given the substantial VRAM headroom, users can experiment with larger batch sizes to maximize throughput. Start with a batch size of 32, as estimated, and incrementally increase it until performance plateaus or memory errors occur. Consider using ONNX Runtime for optimized inference, which can leverage the Tensor Cores effectively. Additionally, monitor the Orin's temperature during extended use, especially in constrained environments, to prevent thermal throttling. If performance is critical, explore quantization techniques like INT8 to further reduce memory footprint and potentially increase processing speed, although this may come with a slight trade-off in accuracy.
For deployment, ensure efficient memory management and minimize unnecessary data transfers between the CPU and GPU. Optimize the input pipeline to pre-process data in parallel and avoid bottlenecks. In edge deployment scenarios, prioritize low-latency inference for real-time applications. If facing resource constraints, consider offloading some pre-processing steps to the CPU.