Which Jetson is best for AI application build?

The rapid rise of Artificial Intelligence (AI) is reshaping industries – starting from smart home automation and ending on healthcare devices – AI is changing the world. This means that teams developing intelligent systems are looking for solutions that will allow them to solve one of their problems – which board is best for AI application build? The answer is: platforms such as NVIDIA Jetson are among the best for AI projects that demand real-time inference, low-power computing, and GPU-accelerated performance. Whether you’re developing an autonomous robot, working on pose estimation, or deploying a machine learning application, selecting the right hardware is essential for achieving reliable and scalable performance.

NVIDIA Jetson boards offer a powerful solution designed specifically for such AI applications. Built to accelerate complex tasks including computer vision, sensor analytics, and deep learning inference, Jetson platforms deliver high performance without compromising on energy efficiency. With comprehensive support for NVIDIA’s software ecosystem – including CUDA (Compute Unified Device Architecture), TensorRT, and the JetPack SDK – Jetson boards streamline the development process from early prototyping through to full-scale production.

The evolution of NVIDIA Jetson boards - from TK1 to Orin

Over the years, NVIDIA has introduced several Jetson boards, each created with particular AI tasks and applications in mind. The journey of Jetson boards started in 2014 with the Jetson TK1, a compact but powerful board built around the Tegra K1 processor, featuring early support for CUDA. This first board made it easier for developers to dive into basic computer vision and robotics projects, running smoothly on Ubuntu Linux right out of the box.

Image: NVIDIA Jetson TK1 Developer Kit

In 2015, Jetson TX1 introduced the more efficient Tegra X1 processor, offering improved GPU performance for embedded AI. This was followed in 2017 by the Jetson TX2, which further enhanced performance and efficiency, supporting deep neural networks while maintaining low power consumption (around 7.5W) – making it suitable for machine learning.

Boards like the TK1, TX1, and TX2 are still excellent for learning and simple AI workloads, but they start to show their limits with real-time inference or more complex neural networks. In 2018, NVIDIA introduced the robust Jetson AGX Xavier, featuring an 8-core CPU and significantly more parallel processing power, ideal for complex edge AI applications, but to meet the growing needs of Artificial Intelligence in 2020 NVIDIA raised the bar even higher and introduced the compact yet powerful Jetson Xavier NX, featuring a 384-core GPU, 48 Tensor Cores, and a 6-core CPU capable of 21 TOPS (Tera Operations Per Second).

For projects where cost is a top concern and compact form matters, NVIDIA introduced in 2019 Jetson Nano. Despite its compact size, Nano is more than capable of handling tasks like object detection, image classification, and face recognition.

Today, Jetson is best for AI applications requiring cutting-edge performance. In fact, the latest generation – the Jetson Orin series launched in 2022 – features up to 2,048 CUDA cores, 64 Tensor Cores, and a 12-core CPU. As a result, delivering up to 275 TOPS, Orin is purpose-built for Artificial Intelligence. It is capable of handling autonomous robotics, sensor fusion, and real-time deep learning in production environments.

Image: NVIDIA Jetson Orin Nano Developer Kit

Which Jetson Board is best for AI projects?

While Jetson TK1, TX1 and TX2 were groundbreaking at the time of their release, they are now considered outdated for many modern AI applications. These models are based on NVIDIA’s earlier Tegra SoC with ARM architecture CPUs and offer basic support for CUDA-enabled parallel processing. They are still capable of handling traditional machine learning tasks, simpler robotics projects, and some low-resolution computer vision work. However, they lack the computational power, memory bandwidth and modern AI software support needed for today’s performance-intensive applications. For instance they may struggle to efficiently run transformer-based models, process high-resolution video streams, or perform real-time multi-object tracking, especially when multiple neural networks need to run simultaneously.

If your project involves advanced neural networks, real-time inference, or edge AI applications that demand both high compute performance and efficiency, your best choice would be one of the Jetson Xavier or Jetson Orin models. These newer boards are specifically designed to support deep learning, computer vision, and other AI tasks related to edge computing devices (e.g. sensors, actuators) – with significantly more TOPS, improved energy efficiency, and full support of NVIDIA’s latest AI SDKs like JetPack (development tools), TensorRT (inference optimization), and DeepStream (video analytics framework), making them ideal for production-level applications.

If your project focuses on a real-time video analysis – such as monitoring customer behavior in the retail store, identifying defects on a manufacturing line, or building a vision-based robot arm for sorting tasks – then the Jetson Xavier NX strikes a solid balance between power and cost. It delivers strong performance in a compact factor form, making it ideal for mid-range robotics, smart security cameras, and IoT AI systems.

However, if you want to make something more advanced – for example, an autonomous delivery robot, an AI-powered agricultural drone, or a multi-sensor industrial inspection system – you’ll want the cutting-edge performance of the Jetson Orin series. With up to 275 TOPS, support for multiple camera inputs, and massive GPU acceleration, Orin modules are ideal for a real-time sensor fusion, complex AI model inference, and deeply developed neural networks run concurrently.

Comparison table of different Jetson models

Model	CPU	GPU	Tensor Cores	TOPS	Memory	Power Usage	Best For
Jetson TK1	Quad-core ARM Cortex-A15	192-core Kepler	–	N/A	2 GB	~10W	Legacy prototyping, basic ML
Jetson TX1	Quad-core ARM Cortex-A57	256-core Maxwell	–	~1.0	4 GB	10–15W	Early AI projects, basic computer vision
Jetson TX2	Quad-core A57 + Dual Denver2	256-core Pascal	–	~1.5	8 GB	7.5–15W	Robotics, drones, moderate AI workloads
Jetson Nano	Quad-core ARM Cortex-A57	128-core Maxwell	–	~0.5	4 GB	5–10W	Entry-level AI, education, hobby projects
Jetson Xavier NX	6-core Carmel ARMv8.2	384-core Volta	48	Up to 21	8 GB / 16 GB	10–15W	Real-time video analytics, compact AI edge devices
Jetson AGX Xavier	Jetson AGX Xavier 8-core Carmel ARMv8.2	512-core Volta	64	Up to 32	16 GB / 32 GB	10–30W	High-end robotics, industrial AI, autonomous machines
Jetson Orin NX	8-core Cortex-A78AE	1024-core Ampere	32	Up to 100	8 GB / 16 GB	10–25W	Advanced robotics, AI inference at the edge
Jetson AGX Orin	12-core Cortex-A78AE	2048-core Ampere	64	Up to 275	32 GB / 64 GB	15–60W	Most demanding AI, autonomous vehicles, real-time multi-sensor AI

Which Jetson is best for AI?

Jetson boards are a powerful and versatile choice for anyone looking to bring AI to life at the edge. Moreover, depending on your project’s requirements and budget, you can choose a board that best aligns with your performance needs. For example, if you’re developing a machine learning application with moderate computational demands, an older model like Jetson TX2 can still be a practical and cost-effective option. However, for more ambitious projects – such as deploying complex deep learning models, real-time analytics, or multi-sensor robotics systems – the Jetson Xavier or Jetson Orin series provides the advanced performance and efficiency required for production-level applications.

With full support for NVIDIA’s latest AI tools and software development kits – including JetPack, TensorRT, and DeepStream – Jetson platforms make it easier than ever to prototype, develop, and deploy intelligent systems at the edge.

Glossary

CUDA (Compute Unified Device Architecture) – This is a parallel computing platform developed by NVIDIA. Consequently, it allows developers to harness the power of NVIDIA GPUs for general-purpose processing.

Tensor – This refers to a multi-dimensional array used in deep learning. In fact, tensors are the fundamental data structure for training and running neural networks.

Tensor Core – specialized hardware within NVIDIA GPUs designed to accelerate tensor operations, such as matrix multiplications

TOPS (Tera Operations Per Second) – a metric that indicates the computational performance of a device, measuring how many trillion operations it can perform per second

SoC (System on a Chip) – an integrated circuit that combines all components of a computer or other system, such as CPU, GPU, memory, and I/O – into a single chip

Edge computing – a computing paradigm where data is processed close to the source (e.g. on a Jetson board near a sensor) instead of being sent to a centralized cloud server