Upgrading from the original Jetson Nano to the Jetson Orin Nano 4GB: how do they compare on GPU architecture, CUDA cores and memory type?

Question

Rapidflare · Accepted Answer

Upgrading from the original Jetson Nano to the Jetson Orin Nano 4GB is a substantial generational leap on all three dimensions you asked about:

| Attribute | Jetson Nano | Jetson Orin Nano 4GB |
|---|---|---|
| GPU architecture | Maxwell | Ampere |
| CUDA cores | 128 | 512 |
| Memory type | 4 GB LPDDR4 | 4 GB LPDDR5 |

**Jetson Nano vs. Jetson Orin Nano 4GB comparison**

Key points:

- **GPU architecture**: The original Nano uses the Maxwell architecture, while the Orin Nano 4GB uses the newer Ampere architecture. The Ampere GPU also brings dedicated Tensor cores (16 of them on the Orin Nano 4GB), which the original Nano does not have, giving you hardware-accelerated AI inference rather than relying on the general-purpose GPU alone.
- **CUDA cores**: The CUDA core count quadruples, from 128 to 512, roughly a 4x increase in raw GPU compute resources.
- **Memory type**: Both modules have 4 GB of memory, but the Orin Nano 4GB moves from LPDDR4 to the faster LPDDR5, with memory bandwidth rising from 25.6 GB/s to 34 GB/s in the default mode (51 GB/s in Super mode).

As context for the upgrade decision, the overall AI performance gap is much larger than the core-count difference suggests: the original Nano delivers 472 GFLOPS FP16, whereas the Orin Nano 4GB delivers up to 20 TOPS in its default mode (34 TOPS in Super mode, where its power envelope expands from 7–10 W to 7–25 W).

Jetson Nano vs Jetson Orin Nano 4GB: GPU architecture, CUDA cores and memory type

Sources

Attribute	Jetson Nano	Jetson Orin Nano 4GB
GPU architecture	Maxwell	Ampere
CUDA cores	128	512
Memory type	4 GB LPDDR4	4 GB LPDDR5