Jetson Orin NX vs AGX Orin: Which Module to Choose

Orin NX vs AGX Orin is the choice between the two upper tiers of NVIDIA’s Jetson Orin family: the Orin NX is a compact, low-power module rated at up to 157 TOPS, and the AGX Orin is the larger module rated at up to 275 TOPS, with four times the memory, twice the memory bandwidth and far more I/O. The right one is the smallest module that holds your models, sensors and frame rate at the power your enclosure can cool.

Both modules use the same Ampere GPU architecture, Arm Cortex-A78AE CPU cores and JetPack software, so the difference is scale, not generation. The Jetson Orin NX packs serious AI performance into a module the size of a laptop memory stick; the Jetson AGX Orin trades size and power for memory, bandwidth and interfaces.

This guide compares them side by side, explains where each one wins, sets the Orin NX 16GB against the AGX Orin 32GB, covers carriers, cooling and lifecycle, and finishes with worked examples and a decision table.

The master table puts the four production modules next to each other. Orin NX figures are shown in their Super modes with the original rating in brackets.

On paper the AGX Orin 64GB has about 1.75× the AI performance of the Orin NX 16GB, but twice the bandwidth and four times the memory, which is why it often scales better than the TOPS ratio suggests on camera-heavy pipelines. TOPS here are sparse INT8; our answer on what TOPS means explains why dense throughput is about half. For every Orin module including Orin Nano and the Industrial version, see our Jetson Orin specs reference.

Interfaces often settle the choice faster than TOPS. These are module capabilities; the carrier board decides which of them reach a connector.

Three of these drive most decisions. Camera lanes set how many GMSL2 or MIPI cameras the module can take before you need virtual channels or a second computer. PCIe lanes set whether a 10GbE NIC, a capture card and NVMe storage can all run at full speed. And 10GbE from the module lets an AGX Orin take lidar or high-resolution GigE cameras without an add-in card.

The Industrial version is the one AGX advantage with no Orin NX equivalent. If the system needs ECC memory or a module rated for the full industrial temperature range, the choice is made before performance comes into it. Our answer on the industrial temperature range explains what such ratings cover.

More performance is not automatically better; it costs size, power, heat and money. For a great many edge AI products, such as one to four cameras, one or two models running in real time and a compact or power-conscious enclosure, the Orin NX has performance to spare and the AGX Orin would be over-specified.

The NX is also the easier module to cool and to fit. The right question is not which is more powerful, but whether the workload needs what the AGX Orin adds. Our answer on Orin Nano vs Orin NX covers the step below, where the deciding factor is often the hardware video encoder. Our guide to choosing a Jetson module walks the whole range, and the Orin Nano Super white paper covers what Super mode did for the smaller modules.

The real decision is often between the largest Orin NX and the smallest AGX Orin, and JetPack 7.2 moved the line. NVIDIA added a MAXN SUPER mode for the AGX Orin 32GB that raises the GPU clock from 930 MHz to 1.3 GHz and lifts it from 200 to 241 TOPS, at up to 60 W.

If a pipeline fits in 16 GB with headroom and needs no more than four cameras, the Orin NX 16GB is the leaner build. If memory use is above about 12 GB, the camera count is rising, or a language model is on the roadmap, the AGX Orin 32GB gives room to grow without the cost of the 64GB module. Measure memory with tegrastats on the real application before deciding, and remember that a Super mode is only useful if the enclosure can hold it; our guide to Jetson cooling covers the heat side.

A closer look at the robot. Two stereo depth streams, positional tracking and a person detector use roughly half the GPU of an Orin NX 16GB and about 8–10 GB of memory, which leaves the module in a 25 W mode with headroom. Adding spatial mapping and a second detector pushes memory past 12 GB and the GPU near its limit, and that is the point to move to the AGX Orin 32GB rather than chase a higher power mode. These figures are planning estimates; resolution, model size and quantisation move them a long way.

The two modules are not drop-in replacements for each other. The Orin NX uses the smaller 260-pin connector it shares with the Orin Nano, while the AGX Orin uses a 699-pin connector, so each needs a Jetson carrier board designed for it. Moving between the two tiers is a board change, not a swap, which is worth knowing before a design commits to one.

The upside of the split is that each pair scales inside its connector. A product line can use Orin Nano and Orin NX on one carrier, and AGX Orin 32GB, 64GB and Industrial on another. Our guide to choosing a Jetson carrier board covers what to check, and our comparison of Jetson Orin vs Xavier covers the move from the previous AGX generation.

Neither module runs at its peak figure all the time. Each has several NVIDIA power modes, set in software, that cap the clocks and the module power: the Orin NX at 10, 15, 25 W and up to 40 W in Super mode, and the AGX Orin at 15, 30 and 50 W and up to 60 W. The mode a system can sustain depends on the heatsink, the enclosure and the ambient temperature at the site.

In practice this matters more than the headline TOPS. An AGX Orin held to 30 W in a small sealed box may deliver less than its datasheet suggests, while an Orin NX at 25 W in a well-designed fanless enclosure can run flat out all day. Size the thermal design for the mode the workload needs, at the highest ambient temperature the system will see, and check it under load before the design is fixed. Our Jetson Super mode explainer covers what the higher power modes change and what they cost in heat.

We fit the module, storage and JetPack image, set the power mode the enclosure can hold and test each system before it ships.