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EDGE AI · NVIDIA JETSON

Jetson Orin Nano vs Orin NX: What Is the Difference?

The Orin Nano delivers up to 67 TOPS in 7 to 25 W with 4 to 8 GB, suited to entry-level, low-power vision. The Orin NX steps up to 157 TOPS in 10 to 40 W with 8 to 16 GB, for multi-camera and heavier inference. Choose the NX when one camera or one model is not enough.

01

The Short Answer

The Orin Nano and Orin NX share the same form factor and connector but sit at different performance tiers. The Nano gives up to 67 TOPS in a 7 to 25 W envelope; the NX roughly doubles that to 157 TOPS in 10 to 40 W, with more memory. The Nano suits single-camera, entry-level vision; the NX handles multi-camera and heavier inference.

02

Where the NX Pulls Ahead

Beyond raw TOPS, the NX carries more memory (8 to 16 GB against 4 to 8 GB) and a higher power ceiling, so it sustains larger models and several concurrent pipelines. If a single camera and a single model fit comfortably on the Nano, there is no need to pay for the NX. The moment the workload grows, the NX is the natural step up without changing form factor.

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    Multiple cameras Concurrent multi-camera perception needs the NX headroom and memory.
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    Larger models 8 to 16 GB lets you run bigger or multiple models the Nano cannot hold.
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    Future headroom If the application is likely to grow, the NX buys room without a redesign.
03

Pin-Compatible by Design

Because the Nano and NX share a connector and form factor, a carrier board designed for one generally accepts the other. That lets you prototype on a Nano and move to an NX, or offer both tiers on one platform, without a board change. We size the module to the workload and build the carrier board and system around it.