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NVIDIA Jetson Orin NX 8 GB

NVIDIA Jetson Orin NX 8 GB: A Hybrid for AI, Robotics, and More

Analysis of Capabilities and Practical Applications in 2025

Introduction

The NVIDIA Jetson Orin NX 8 GB is a compact yet powerful module designed for AI solution developers, robotics, and edge computing. However, its Ampere-based architecture and support for CUDA also attract enthusiasts interested in using it for unconventional scenarios. In this article, we will explore what this GPU is capable of, what tasks it can handle, and who it is suitable for in 2025.

1. Architecture and Key Features

Architecture: The foundation of the Jetson Orin NX is a hybrid platform featuring a GPU based on the Ampere architecture and a 6-core ARM Cortex-A78AE CPU. The manufacturing process is 5 nm, ensuring high energy efficiency.

Unique Features:

- 4th Generation Tensor Cores for accelerating AI inference (up to 100 TOPS).

- Support for RTX technologies (ray tracing) and DLSS in a limited format due to compatibility with NVIDIA’s API.

- NVIDIA JetPack SDK — optimized for use with ROS 2, computer vision, and neural networks.

Important: Unlike desktop GPUs, this device focuses on parallel computing for AI rather than graphics.

2. Memory: Type, Size, and Impact on Performance

- Type: LPDDR5 (as opposed to GDDR6 used in gaming cards).

- Size: 8 GB with a bandwidth of 102 GB/s.

- Features: Low memory latency is beneficial for AI tasks, but limited bandwidth lowers performance in gaming and 3D rendering.

For comparison, the desktop RTX 4060 with GDDR6 (128-bit, 272 GB/s) offers 2.5 times more bandwidth.

3. Game Performance: Realistic Expectations

The Jetson Orin NX is not marketed as a gaming card, but in 2025 it is being tested in less demanding projects:

- 1080p / Low:

- CS:2 — 45–55 FPS (without ray tracing).

- Fortnite — 30–40 FPS (DLSS in Performance mode).

- Cyberpunk 2077 — 18–25 FPS (Low, without RT).

- Ray Tracing: Activating RT reduces FPS by 40–60%, making gameplay uncomfortable.

Conclusion: The device is suitable for indie games or streaming from cloud services, but not for AAA projects.

4. Professional Tasks: Strength in AI and Robotics

- Video Editing: Accelerates rendering in DaVinci Resolve via CUDA, but 8 GB of memory limits handling 4K footage.

- 3D Modeling: In Blender Cycles, rendering moderately complex scenes is 20–30% slower than with the RTX 3050.

- Scientific Calculations:

- Ideal for neural network inference (YOLOv8, GPT-Nano) thanks to Tensor Cores.

- Supports CUDA and OpenCL, but falls short against specialized GPUs in tasks like CFD modeling.

Advice: It performs best in embedded projects, such as autonomous drones or computer vision systems.

5. Power Consumption and Thermal Output

- TDP: 15–25 W (operating modes configurable via JetPack).

- Cooling:

- Passive heatsinks are suitable for basic tasks.

- For prolonged workloads (AI training), active cooling is required (e.g., Noctua NF-A4x20 fans).

- Cases: Compact solutions with ventilation are recommended (NVIDIA suggests Jetson-compatible enclosures from companies like Connect Tech).

6. Comparison with Competitors

- NVIDIA RTX A2000 (12 GB): Desktop GPU with GDDR6 (384 GB/s) stronger in 3D rendering but consumes 70 W. Price: $600+.

- AMD Ryzen V2000: Embedded APU with Radeon Vega 8. Weaker in AI performance, but cheaper ($250).

- Raspberry Pi 5 AI Kit: Budget option for simple tasks, but without CUDA support.

Verdict: The Orin NX is a middle ground for projects that require AI and mobility.

7. Practical Tips

- Power Supply: 100–150 W is sufficient (e.g., Meanwell EPP-200).

- Compatibility:

- OS: Linux (Ubuntu 24.04 LTS with JetPack 6.0).

- Platforms: ROS 2, Docker, Kubernetes.

- Drivers: Update via NVIDIA SDK Manager — third-party builds may disrupt AI libraries.

8. Pros and Cons

Pros:

- NVIDIA ecosystem (CUDA, TensorRT, Isaac SDK).

- Low power consumption.

- Compact size (70×45 mm).

Cons:

- Limited gaming performance.

- High price for embedded solutions ($499).

- Challenges with memory upgrades.

9. Final Conclusion: Who is the Jetson Orin NX 8 GB For?

This module is designed for:

- AI/robotics developers who need a portable GPU for prototyping.

- Edge computing enthusiasts, for example, for smart cameras or drones.

- Educational projects (labs, machine learning courses).

Avoid the Orin NX if you need gaming, 4K video editing, or complex 3D rendering. Its strength lies in AI, automation, and pushing the boundaries of innovation.

Price in 2025: $499 (new retail version).

Alternative: For gaming and creativity, consider the RTX 4050 Mobile or AMD Radeon 7600M XT.

Basic

Label Name

NVIDIA

Platform

Professional

Launch Date

March 2023

Model Name

Jetson Orin NX 8 GB

Generation

Tegra

Bus Interface

PCIe 4.0 x4

Transistors

Unknown

Tensor Cores

Tensor Cores are specialized processing units designed specifically for deep learning, providing higher training and inference performance compared to FP32 training. They enable rapid computations in areas such as computer vision, natural language processing, speech recognition, text-to-speech conversion, and personalized recommendations. The two most notable applications of Tensor Cores are DLSS (Deep Learning Super Sampling) and AI Denoiser for noise reduction.

TMUs

Texture Mapping Units (TMUs) serve as components of the GPU, which are capable of rotating, scaling, and distorting binary images, and then placing them as textures onto any plane of a given 3D model. This process is called texture mapping.

Foundry

Samsung

Process Size

8 nm

Architecture

Ampere

Memory Specifications

Memory Size

8GB

Memory Type

LPDDR5

Memory Bus

The memory bus width refers to the number of bits of data that the video memory can transfer within a single clock cycle. The larger the bus width, the greater the amount of data that can be transmitted instantaneously, making it one of the crucial parameters of video memory. The memory bandwidth is calculated as: Memory Bandwidth = Memory Frequency x Memory Bus Width / 8. Therefore, when the memory frequencies are similar, the memory bus width will determine the size of the memory bandwidth.

128bit

Memory Clock

1600MHz

Bandwidth

Memory bandwidth refers to the data transfer rate between the graphics chip and the video memory. It is measured in bytes per second, and the formula to calculate it is: memory bandwidth = working frequency × memory bus width / 8 bits.

102.4 GB/s

Theoretical Performance

Pixel Rate

Pixel fill rate refers to the number of pixels a graphics processing unit (GPU) can render per second, measured in MPixels/s (million pixels per second) or GPixels/s (billion pixels per second). It is the most commonly used metric to evaluate the pixel processing performance of a graphics card.

12.24 GPixel/s

Texture Rate

Texture fill rate refers to the number of texture map elements (texels) that a GPU can map to pixels in a single second.

24.48 GTexel/s

FP16 (half)

An important metric for measuring GPU performance is floating-point computing capability. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy.

3.133 TFLOPS

FP64 (double)

An important metric for measuring GPU performance is floating-point computing capability. Double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy, while single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.

783.4 GFLOPS

FP32 (float)

An important metric for measuring GPU performance is floating-point computing capability. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.

1.598 TFLOPS

Miscellaneous

SM Count

Multiple Streaming Processors (SPs), along with other resources, form a Streaming Multiprocessor (SM), which is also referred to as a GPU's major core. These additional resources include components such as warp schedulers, registers, and shared memory. The SM can be considered the heart of the GPU, similar to a CPU core, with registers and shared memory being scarce resources within the SM.

Shading Units

The most fundamental processing unit is the Streaming Processor (SP), where specific instructions and tasks are executed. GPUs perform parallel computing, which means multiple SPs work simultaneously to process tasks.

1024

L1 Cache

128 KB (per SM)

L2 Cache

256KB

TDP

20W

Vulkan Version

Vulkan is a cross-platform graphics and compute API by Khronos Group, offering high performance and low CPU overhead. It lets developers control the GPU directly, reduces rendering overhead, and supports multi-threading and multi-core processors.

1.3

OpenCL Version

3.0

OpenGL

4.6

DirectX

12 Ultimate (12_2)

CUDA

8.6

Shader Model

6.7

ROPs

The Raster Operations Pipeline (ROPs) is primarily responsible for handling lighting and reflection calculations in games, as well as managing effects like anti-aliasing (AA), high resolution, smoke, and fire. The more demanding the anti-aliasing and lighting effects in a game, the higher the performance requirements for the ROPs; otherwise, it may result in a sharp drop in frame rate.

Benchmarks

FP32 (float)

Score

1.598 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS

Radeon RX Vega 10 Mobile

1.698 +6.3%

Radeon R9 360 OEM

1.645 +2.9%

Jetson Orin NX 8 GB

1.598

Radeon RX 550X Mobile

1.535 -3.9%

GeForce GTX 960M

1.475 -7.7%