NVIDIA Jetson Orin Nano system-on-module (SoM) is an update to the Jetson Nano entry-level Edge AI and robotics module that delivers up to 40 TOPS of AI performance, meaning it’s up to 80 times faster than the original module.
The new SoM features an hexa-core Arm Cortex-A78AE processor, an up to 1024-core NVIDIA Ampere architecture GPU with 32 Tensor cores, up to 8GB RAM, and the same 260-pin SO-DIMM connector found in the Jetson Orin NX modules.
Two versions are offered with the following specifications:
Jetson Orin Nano 4GB
Jetson Orin Nano 8GB
6-core Arm Cortex-A78AE v8.2 64-bit CPU @ 1.5 GHz with 1.5 MB L2 + 4 MB L3
512-core NVIDIA Ampere GPU @ 625 MHz with 16 Tensor cores
1024-core NVIDIA Ampere GPU @ 625 MHz with 32 Tensor cores
20 Sparse TOPs | 10 Dense TOPs
40 Sparse TOPs | 20 Dense TOPs
4GB 64-bit LPDDR5 34 GB/s
8GB 128-bit LPDDR5 68 GB/s
Supports external NVMe
1x 4K60 (H.265) | 2x 4K30 (H.265) | 5x 1080p60 (H.265) | 11x 1080p30 (H.265)
1080p30 supported by 1-2 CPU cores (i.e. software encoding)
1x 4K30 multimode DisplayPort 1.2 (+MST)
1x embedded DisplayPort 1.4
1x HDMI 1.4
Up to 4x cameras (8x through virtual channels)
8 lanes MIPI CSI-2 D-PHY 2.1 (up to 20 Gbps)
3x USB 3.2 Gen2 (10 Gbps)
3x USB 2.0
1 x4 + 3 x1 PCIe Gen3, Root Port, & Endpoint
3x UART, 2x SPI, 2x I2S, 4x I2C, 1x CAN, DMIC and DSPK, PWM, GPIOs
5W or 10W
7W or 15W
69.6 mm x 45 mm 260-pin SO-DIMM connector
That means the Jetson Orin family has now six modules ranging from 20 TOPS to 275 TOPS. There’s no specific development kit for the Jetson Orin Nano SoM since it can be emulated on the NVIDIA Jetson AGX Orin developer kit, and supported by the JetPack 5.0.2 SDK based on Ubuntu 20.04.
NVIDIA has tested some dense INT8 and FP16 pre-trained models from NGC and a standard ResNet-50 model on the new module and compared the results against the earlier generation entry-level modules such as the Jetson Nano and Jetson TX2 NX.
That’s a significant increase in performance, as well as in price since the Jetson Orin Nano series production modules will be available in January for $199 for the Jetson Orin Nano 4GB and $299 for the Jetson Orin Nano 8GB. Those are unit prices for 1K orders. More details may be found on the announcement and developer website.
Jean-Luc started CNX Software in 2010 as a part-time endeavor, before quitting his job as a software engineering manager, and starting to write daily news, and reviews full time later in 2011.
9 Replies to “$199+ NVIDIA Jetson Orin Nano system-on-module delivers up to 40 TOPS”
Ouch, such performance will run circles around the Rk3588 based boards, not only in CPU performance, but also the additional powerful npu. Just wonder if I can put it in my original Jetson nano developer carrier board? They only say it’s compatible with the Orin nx, which may be different from the original nano?
> run circles around the Rk3588 based boards, not only in CPU performance
The A78AE are only clocked at 1.5GHz and there are just six of them (most probably the result of binning/QA at the factory).
If if it’s not about GPGPU/NPU single-threaded performance is lower compared to RK3588’s A76 @ ~2.3 GHz and with most CPU-bound workloads even multi-threaded could be lower.
Have you seen benchmarks? I’ve estimated about 20-30% better performance per core of A-76 vs A-78, although the clock speed may eat that up. But then there are six vs four performance cores. Also possibly faster memory.
Maybe I should withdraw “run circles”, but the nano certainly is interesting, especially when running machine learning models.
> Have you seen benchmarks?
Both times comparing the 12-core Orin at 2.2 GHz with RK3588 at 2350/1800 MHz: Geekbench numbers here, 7-zip, tinymembench, ramlat, openssl and cpuminer there (ARMv8 Crypto Extensions perform identical on A72-A78 and scale linearly with clockspeeds).
That does look very good for the Rk3588. Given that the 12-core Orin is in a completely different price league. Now my board just needs to be shipped.
as always, thanks! awesome
> Have you seen benchmarks?
Sure, Results.md in my sbc-bench repo list 7-zip, tinymembench, ramlat, openssl and cpuminer scores. And this here is Geekbench.
Always comparing the 12-core Orin at 2.2 GHz with RK3588 at 2350/1800 MHz. I wouldn’t be too surprised if memory performance with little Jetson Orin Nano is (significantly) lower.
Only software encode? very unlike previous Jetson series…
This looks to me as a software/Jetpack issue, as I cannot imagine, why GPU cannot be used for it.