前沿推理走向边缘：如何在 NVIDIA Jetson 上部署并优化模型
Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson
NVIDIA 介绍如何在 Jetson 边缘硬件上部署并优化前沿推理与智能体模型。此前具备多步推理能力的模型体积过大，无法在边缘本地运行，开发者只能把推理绕经数据中心，带来网络依赖、成本上升和本可留在设备上的数据外泄风险。NVIDIA 表示这一限制正在被解除。
Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...
Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run locally on edge hardware. Developers building agents have had to route inference through a data center, adding network dependency, increasing costs, and exposing data that may need to stay on device. That constraint is lifting.
来源：NVIDIA Developer Blog · developer.nvidia.com