Nvidia Jetson Orin Nano 2
Nvidia Jetson Orin Nano 2
When it comes to artificial intelligence, today we still mostly think of large data centers, servers consisting of thousands of GPUs, and giant models running in the cloud. Yet AI's next big leap forward may come not just from building more powerful models, but from bringing those models into the physical world. Artificial intelligence becomes something much different when robots, drones, security cameras, autonomous machines and industrial systems can process images, sound and surrounding data on themselves, rather than simply connecting to the internet and waiting for a response from a remote server.
Nvidia's new Jetson Orin Nano 2 platform was developed exactly for this transformation. The company promises up to two times higher inference performance and 40 percent lower energy consumption compared to the previous Jetson Orin Nano Super, while significantly strengthening its entry-level edge AI and robotics platform in the same compact form factor. Nvidia's goal is quite clear: to bring advanced generative AI and vision-language models into the robots of not only giant laboratories, but a much wider range of developers.
What is Jetson and Why Is It So Important to Nvidia?
It is necessary to distinguish the Jetson family from classic mini PCs. These systems are not just small computers; Embedded computing platforms designed directly for robotics, computer vision and edge AI.
A Jetson system could analyze the image from a camera on a factory production line, help a drone recognize objects in its environment, enable a small home robot to talk to a user, or help a mobile device understand its environment in real time.
The important concept here is edge AI.
In a cloud AI system, data is first sent to a remote data center. The model processes the data and returns the result. In Edge AI, the process takes place directly at the point where the device is located.
Although this difference seems small, it is critical for robotics.
We do not want a robot to wait for a response from the cloud for hundreds of milliseconds when a human appears in front of it. If a security camera constantly sends its entire image to the internet, it may cause problems in terms of privacy and bandwidth. It is also unacceptable for a drone to become completely dysfunctional when disconnected.
Therefore, the growth of physical AI requires the expansion of AI processing power from data centers to devices.
The Jetson family is one of Nvidia's main platforms in this field.
How Powerful is Jetson Orin Nano 2?
The new Jetson Orin Nano 2 delivers up to 78 TOPS of intense INT8 AI performance within a 40W power envelope. Nvidia states that the device approximately doubles the inference performance of the previous Orin Nano Super, thanks to its improved Tensor Core structure and increased memory bandwidth.
TOPS means “trillion operations per second”, that is, trillion operations per second. However, this figure should not be considered as a quality indicator alone, like GHz in desktop processors. The type of data used by the model, software optimization, memory bandwidth and the actual workload of the system can significantly change the result.
Still, a value like 78 TOPS is quite remarkable for a device of this size and power consumption.
The current Jetson Orin Nano Super development kit goes up to approximately 67 TOPS. The new generation does not only increase peak performance; Nvidia specifically highlights the nearly two-fold increase in real inference work.
The Real Innovation Performance Per Watt
In robotic systems, the only question is "how fast?" The question is not enough.
One of the more important questions:
How much energy does he use to achieve this performance?
A GPU in a data center can consume hundreds of watts. However, the little robot has a battery. The drone tries to stay in the air. Industrial equipment may use passive or limited cooling.
Here is the important feature of Jetson Orin Nano 2.
According to Nvidia, the new model can provide the performance of the previous generation in 15W mode while consuming 40 percent less energy. This improvement in watts per performance while maintaining the same physical size may be more valuable than the raw TOPS increase, especially for mobile robots.
Every watt consumed in robot design turns into another problem.
Bigger battery.
Heavier body.
More cooling.
Shorter working time.
Therefore, energy efficiency directly affects product design.
8 GB Memory Seems Small, But It's How You Use It
Jetson Orin Nano 2 has 8 GB LPDDR5X memory and approximately 120 GB/s memory bandwidth. The previous Orin Nano Super development kit featured 8 GB LPDDR5 and 102 GB/s bandwidth.
In the world of desktop AI, 8 GB may seem too small today. This is quite limited in an era when large language models require hundreds of gigabytes of memory.
However, edge AI's approach is different.
The aim here is not to run the world's largest model most of the time.
Solving a specific task with the smallest and most efficient model possible.
Hundreds of billions of parameters may not be needed for a robot to recognize the person in front of it. A small vision model may be sufficient to detect a faulty part on a production line. Quantized models can be used for simple speech and image understanding tasks of a home robot.
Nvidia also positions Orin Nano 2 with models optimized especially for memory-efficient edge inference.
This shows us the other side of the AI industry.
Racing in the cloud “how big is the model?” While racing at the edge most of the time:
How small a model can we do the same job with?
Nvidia Cosmos, Nemotron, Gemma 4 and Qwen 3 on Device
In Nvidia's official statement, it is stated that Jetson Orin Nano 2 will support open models such as Nvidia Cosmos, Nvidia Nemotron, Gemma 4 and Qwen 3 with optimized versions.
This is very important because the Jetson platform is no longer just a classic object detection device.
Old edge AI scenarios mostly involved jobs like:
“Did the camera see people?”
“Is this part faulty?”
“Is there a vehicle in front of us?”
In the new generation, the robot can simultaneously understand the image, process natural language and explain its work.
For example, the user tells the robot:
“Bring the red cup on the table.”
When you say that, it is not enough for the system to just understand the speech.
Must understand the concept of “red cup”.
He must see the table from the camera.
It must detect the object.
He must determine his position.
Must create an action plan.
And it must accomplish the task.
In other words, language + vision + robotics are starting to combine in the same system.
Why is there such a need for Vision-Language Model Robots?
Vision-language models are AI models that can associate image and language within the same system.
A conventional camera system can tell that there is a “dog” in the image.
The vision-language model is:
“The little brown dog standing in front of the door is looking to the right.”
It can produce a more contextual interpretation such as:
This is critical for robotics.
Because the real world does not consist only of categories.
Location.
Relationship.
Context.
Movement.
Purpose.
These need to be understood together.
The ability of devices like the Jetson Orin Nano 2 to run more powerful generative and multimodal models could help robots go beyond a few pre-programmed commands.
Why is Reachy Mini Demo Important?
Nvidia showed off a running demo on the Reachy Mini robot to demonstrate the new system. In this scenario, the robot can run the language model, speech model and real-time visual perception systems together on the same device.
The value of this show is bigger than just “the little robot can talk.”
In the past, each of these tasks might have required a separate system or cloud service.
Speech-to-text.
LLM.
computer vision
Text-to-speech.
The Jetson approach tries to move more of them into the same edge computer.
This can make the robot less dependent on the internet and reduce response time.
Is the ChatGPT Moment for Robots Coming?
After 2022, we may begin to experience a similar transformation in robotics as experienced in large language models.
Before ChatGPT, there were natural language models.
But it was difficult to use.
Then the models became powerful enough and easy to use, and suddenly millions of people were using AI in their daily lives.
Computer vision, SLAM, motion planning and speech recognition systems have been available in robotics for many years.
What was missing was the layer that brought them together in a meaningful and flexible way at the same time.
Multimodal and agentic AI systems can become this missing piece.
Devices such as Jetson Orin Nano 2 are one of the hardware layers that bring these models to the real world.
So the product is not just a faster Jetson.
A small sign that physical AI is getting cheaper.
What is Physical AI?
Nvidia has been using the term physical AI more and more lately.
Generative AI produces digital output for us.
Text.
Visual.
Video.
Code.
Physical AI, on the other hand, perceives and reacts to the physical world.
Robot arm.
Autonomous vehicle.
Drone.
Humanoid robot.
Warehouse system.
Smart camera.
These systems translate AI's decisions into real-world actions.
The cost of error is much higher here.
A chatbot may give the wrong answer.
If a robot moves incorrectly, it can cause physical harm.
Therefore, latency, security, sensor integration and real-time processing become critical.
This is exactly why edge computing is a core part of physical AI.
Difference Between Cloud AI and Edge AI
There are a few problems with having a robot send every question to the data center.
The first is latency.
A robot may have to react to moving objects in its environment within milliseconds.
Second is connection.
Wi-Fi or mobile connection is not always reliable.
Third is privacy.
If the home robot sends everything it sees from the camera to the cloud, it may pose a serious privacy problem for the user.
The fourth is cost.
Using cloud inference service 24/7 by thousands of robots can result in serious API and data center costs.
Therefore, most future systems are likely to be hybrid.
Fast and private operations on the device.
Very heavy operations are in the cloud.
Jetson targets the edge side of this hybrid structure.
What Can a Small Business Do with Jetson?
Jetson's platform isn't just for billion-dollar companies making humanoid robots.
There are many smaller-scale uses.
A camera system that analyzes the occupancy of shelves can be installed in a retail store.
Product defects can be detected in real time in a manufacturing facility.
Certain operations can be observed in a restaurant or kitchen.
In the field of agriculture, plant diseases can be classified through images.
Product counting can be done in the warehouse.
Certain structures can be identified from the drone image.
Local human and object analysis can be performed on security cameras.
And in all of these, it may not be necessary to constantly send the image out.
This is especially valuable for businesses where data privacy is important.
Is Edge AI Used in Advertising and Retail?
Here is one of the interesting aspects of the issue for Voldi Creative.
Edge AI isn't just about industry or robotics.
Physical stores can also turn into smart systems.
For example, a digital screen can only analyze the approximate gaze duration without recording the face of the person passing in front of it.
It can be measured which product stand attracts more attention.
Density within the store can be mapped.
An interactive installation can react in real time to the viewer's movements.
Generative visuals may change depending on the visitor's movements at an event.
In the museum or brand experience area, the user can communicate with the digital system by speaking and moving.
Not every one of these has to be connected to a major cloud service.
Edge computers like Jetson can be an invisible layer of technology within physical advertising experiences.
Does the New Generation Billboard Have to See You?
We're getting into slightly more interesting design territory here.
In future DOOH systems, advertising may not just be the video being played.
Can understand the crowd density in front of the screen.
It can react to the weather.
It can analyze real-time traffic data.
Can interact visually with surrounding objects.
It is theoretically possible to do all this without collecting personal identification.
For example, the system asks “who is this person?” He never asks the question.
It only evaluates anonymous data such as “there are currently three people in front of the screen and two of them are looking at the image.”
Low-latency edge AI may become increasingly important in such physical-digital advertising systems.
Camera Systems Will Become Smarter with Jetson Orin Nano 2
Computer vision is one of the most natural areas of use for the Jetson family.
Increasing AI performance in the next generation may allow more camera streams to be analyzed simultaneously or to run more advanced models.
However, the concept of “smart camera” does not only mean facial recognition.
Quality control.
Object tracking.
Anomaly detection.
Pose analysis.
Optical character recognition.
Traffic analysis.
Robot orientation.
These are all areas of computer vision usage.
With generative and vision-language models coming to the edge, the user can now get not only coordinates but also descriptions in natural language from the system.
Nvidia's Real Advantage Isn't Hardware, It's Ecosystem
The technical features of Jetson Orin Nano 2 are important.
But Nvidia's biggest advantage is not just offering 78 TOPS.
CUDA.
TensorRT.
JetPack.
Isaac.
Cosmos.
Nemotron.
ROS integrations.
Ready model optimizations.
Carrier board manufacturers.
Developer community.
According to Nvidia's statement, more than 3 million developers are working on the company's robotics stack. Companies such as Cognex, Doosan Bobcat, Matic and Wing are also among the first partners to evaluate Orin Nano 2 in real-world systems.
This ecosystem is very valuable for a hardware company.
Because when a developer makes a new product, he doesn't just buy processors.
It also purchases the software infrastructure that has been developed for years.
We see a scaled-down version of Nvidia's strategy with CUDA in the data center with Jetson in robotics.
Does It Compare to Raspberry Pi?
Due to physical size, Jetson devices are often compared to Raspberry Pi.
But their goals are different.
Raspberry Pi is a general-purpose low-cost development computer.
Jetson, on the other hand, is a much more special platform designed for GPU-accelerated AI inference.
For a simple sensor system, web server or electronics prototype, the Raspberry Pi may be more economical.
For real-time vision model, multimodal AI or robotic perception workload, Jetson is in a much more suitable class.
So “which is better?” instead of:
For which problem?
It is necessary to ask.
Is Using Ampere Backward?
There is an interesting technical detail here.
Jetson Orin Nano 2 still uses the Ampere GPU architecture. This may seem strange at first glance, considering that Nvidia has moved to much newer architectures on the desktop and data center side.
However, product cycles are different in embedded systems.
A robotics manufacturer does not want to design a system today and replace the entire hardware platform next year.
Long term support.
Thermal behavior.
Software stability.
Carrier board compatibility.
These may be more important than raw architectural innovation.
Nvidia's plan to keep existing modules of the Jetson Orin platform available until 2032 also demonstrates this long product cycle approach.
So in the world of edge AI, “latest GPU architecture” is not always the most important criterion.
Price Not Announced Yet
Nvidia has not yet announced official prices for the Jetson Orin Nano 2 module and Developer Kit. The company states that the products will be available in the first half of 2027.
For comparison, the official price of the current Jetson Orin Nano Super Developer Kit is $249. How long the new model will maintain the same entry-level positioning will be important for the product to reach a wide developer base.
Because the real success of Jetson Orin Nano 2 does not depend only on its performance.
It depends on its accessibility.
As the cost of developing robotics falls, more small teams can develop physical AI products.
The Next Big Race in the AI World Could Be Robotics
When we look at the development of generative AI in the last few years, we see an incredible pace.
Text first.
Then visual.
Video.
Voice.
Code.
Now, all these capabilities are combined in a single system.
He can see a pattern.
He can listen.
He can talk.
He can reason.
He can drive.
What's missing is physical movement.
Robotics is the continuation of this.
As AI transitions from being a digital assistant on a screen to a system that moves in the physical world, where the processing power resides must also change.
It's not practical to connect every robot in the world to a giant Nvidia data center.
Some of the AI has to go inside the robot.
Jetson Orin Nano 2 is one of the small but important hardware designed for this.
Why Do We Find Jetson Orin Nano 2 Interesting From Voldi Creative's Perspective?
We are not only interested in Nvidia releasing a new development card here.
The bigger change is this:
The border between digital experience and physical experience is disappearing.
Until today, we were designing an interactive website.
We will design an interactive store soon.
Today, the user changes the object on the screen with the mouse movement.
Tomorrow, when he walks in physical space, the digital system around him may react to him.
Today the chatbot speaks to the brand.
Tomorrow, the brand mascot may greet the customer as a physical robot.
Today the camera is recording video.
Tomorrow, the camera can understand what it sees and change the design system in real time.
The potential for this for the creative sector is enormous.
Because AI will no longer be just in Photoshop, Premiere or the browser.
It will be inside the venue.
Jetson Orin Nano 2 will not create this future alone. However, the fact that a small 40-watt device can run image, speech and language models at the same time clearly shows the direction in which technology is moving.
Once upon a time, the big question of artificial intelligence was:
“Can the machine think?”
Today we have largely stopped discussing the answer to this question.
The new question increasingly becomes:
Can the machine understand what it sees and take action in the real world?
Nvidia's Jetson family works exactly on the hardware side of this problem.
And the next big revolution in artificial intelligence will not happen on the screen, but perhaps inside the machines moving right in front of us.
Nur Oğuz