The NVIDIA Jetson Orin Nano 8GB boasts an Ampere architecture with 1024 CUDA cores and 32 Tensor cores, making it a capable platform for AI inference despite its low 15W TDP. Its 8GB of LPDDR5 memory provides ample space for running models like CLIP ViT-H/14, which requires approximately 2.0GB of VRAM when using FP16 precision. This leaves a substantial 6GB VRAM headroom, allowing for larger batch sizes or the simultaneous execution of other smaller tasks. The memory bandwidth of 70 GB/s, while not exceptionally high, is sufficient for feeding data to the GPU cores for this particular model, preventing significant bottlenecks during inference.
CLIP ViT-H/14's relatively modest 0.6B parameters and 77-token context length further contribute to its suitability for the Orin Nano. The Tensor Cores will be effectively utilized for the matrix multiplications inherent in the attention mechanism of the Vision Transformer, leading to accelerated performance. While a high-end desktop GPU would offer significantly higher throughput, the Orin Nano provides a compelling balance of performance and power efficiency for edge deployment scenarios.
For optimal performance, utilize TensorRT or ONNX Runtime to further optimize the CLIP ViT-H/14 model for the Jetson Orin Nano. Experiment with different batch sizes to find the sweet spot between throughput and latency. Monitor GPU utilization and memory usage to ensure efficient resource allocation. Consider quantizing the model to INT8 if higher throughput is required, although this may come at the cost of some accuracy. Ensure sufficient cooling for the Jetson Orin Nano, especially during sustained inference workloads, to prevent thermal throttling.