Jetson Deployment

Key Insight

Deploying deep learning models on resource-constrained edge hardware forces developers to optimize for a fundamentally different set of trade-offs than cloud or desktop inference. An NVIDIA Jetson board packs a CUDA-capable GPU into a module that runs on 5–40 watts — orders of magnitude less than a desktop card — by sharing a single pool of memory between CPU and GPU (eliminating PCIe transfer overhead). Quantizing a model to INT8 or FP8 precision and compiling it with TensorRT squeezes maximum performance from this limited hardware, enabling real-time inference for robotics and computer vision applications where low latency and power efficiency matter more than raw throughput.