Home / NVIDIA / NVIDIA Jetson AGX Orin 32 GB: Performance and Specs

NVIDIA Jetson AGX Orin 32 GB

NVIDIA Jetson AGX Orin 32 GB: Review and Analysis of Capabilities in 2025

1. Architecture and Key Features

The NVIDIA Jetson AGX Orin is not a traditional graphics card but a compact computing module designed for artificial intelligence (AI), robotics, and edge computing tasks. At its core is the Ampere architecture, the same used in NVIDIA's professional RTX Axxx series GPUs. It is built on a 8 nm process from Samsung, ensuring a balance between performance and energy efficiency.

The module is equipped with 2048 CUDA cores, 64 Tensor cores to accelerate AI algorithms, and 2 GPU video analytics accelerators (encoding/decoding up to 8K). Notable unique features include support for DLSS (Deep Learning Super Sampling) for real-time image quality enhancement, but ray tracing (RTX) is absent—Jetson AGX Orin is not intended for gaming rendering.

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

The module utilizes 32 GB LPDDR5 with a bandwidth of 204.8 GB/s. This is not GDDR6/X or HBM—LPDDR5 is optimized for energy efficiency rather than high gaming loads. This memory volume is ideal for processing large neural networks (e.g., ResNet-50 or BERT) and simultaneously running multiple AI models.

For professional tasks (rendering, simulations), the bandwidth is sufficient, but in gaming or 4K editing, there can be "bottlenecks" due to the lack of high-speed video memory.

3. Gaming Performance: Realistic Expectations

The Jetson AGX Orin is not marketed as a gaming GPU, but can be used for streaming or running light projects. In CS:GO at Low/1080p settings, the module achieves around 40-50 FPS, and in Minecraft—up to 60 FPS. However, modern AAA games like Cyberpunk 2077 or Starfield run poorly on it (less than 15 FPS, even at 720p).

DLSS support partially compensates for the lack of power, but the absence of RT cores makes ray tracing unavailable. For gaming, it is better to opt for desktop GPUs—such as the RTX 4060 or AMD Radeon RX 7600.

4. Professional Tasks: Where Jetson AGX Orin Excels

The main strength of the module lies in accelerating AI and professional workflows:

- Video Editing: Hardware encoding for AV1/HEVC allows for processing 8K footage in DaVinci Resolve with minimal latency.

- 3D Modeling: In Autodesk Maya, rendering medium scenes takes 30% less time compared to Jetson Xavier.

- Scientific Computations: CUDA and cuDNN speed up simulations in MATLAB or training neural networks (e.g., 1 hour on AGX Orin versus 2 hours on the previous generation).

For serious rendering tasks (Blender Cycles, Unreal Engine 5), RTX A6000 or AMD Radeon Pro W7800 are better suited, but Jetson wins in portability.

5. Power Consumption and Heat Dissipation: Efficiency First

The module's TDP ranges from 15 W (power-saving mode) to 50 W (maximum performance). Its built-in heatsink and passive cooling make it ideal for drones, medical devices, or autonomous robots.

For stationary use, cases with active cooling (e.g., from Seeed Studio) are recommended, especially under prolonged loads.

6. Comparison with Competitors: A Niche in Embedded Solutions

There are few direct analogs to the Jetson AGX Orin. Notable competitors include:

- AMD Ryzen Embedded V3000—strong in multi-threaded CPU tasks but weaker in AI.

- Intel Movidius Myriad X—cheaper ($500), but limited to 16 GB of memory and lacks CUDA support.

- Qualcomm RB5—focused on IoT, but not suitable for complex neural networks.

Among NVIDIA's offerings, the closest "relative" is the RTX A2000 (12 GB GDDR6, 70 W), but it requires a PCIe slot and is not suitable for embedded systems.

7. Practical Tips: How to Integrate Jetson AGX Orin

- Power Supply: 65 W is sufficient (via USB-C), but for peripherals (cameras, sensors), it is better to opt for a higher capacity—90 W.

- Platforms: Officially supports Linux (JetPack SDK 6.0) and Docker. Windows is possible via virtualization.

- Drivers: Update them through NVIDIA Developer Zone—optimizations for new AI frameworks (PyTorch, TensorFlow) are frequently released here.

8. Pros and Cons: Balancing Capabilities

Pros:

- Best-in-class performance per watt for AI tasks.

- Compactness and passive cooling.

- Support for modern codecs (AV1, H.265).

Cons:

- Not suitable for gaming and high-level 3D rendering.

- High price ($1799 in 2025).

- Limited ecosystem compared to desktop GPUs.

9. Final Conclusion: Who Is the Jetson AGX Orin Suitable For in 2025?

This module is the perfect choice for:

- AI Developers creating autonomous systems (drones, delivery robots).

- Medical Startups working with image processing (MRI, microscopy).

- Engineers needing a portable platform for algorithm testing.

If you are looking for a GPU for gaming or working in Adobe Premiere—consider the GeForce RTX 4070 or Radeon RX 7700 XT. The Jetson AGX Orin is a specialized tool that shines where mobility and efficiency are required, rather than versatility.

Prices are current as of April 2025. The listed price is the recommended cost of new devices.

Basic

Label Name

NVIDIA

Platform

Professional

Launch Date

February 2023

Model Name

Jetson AGX Orin 32 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

32GB

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.

256bit

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.

204.8 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.

22.32 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.

52.08 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.

6.666 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.

1.667 TFLOPS

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.

3.4 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.

1792

L1 Cache

128 KB (per SM)

L2 Cache

256KB

TDP

40W

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

3.4 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS

Quadro T2000 Mobile

3.729 +9.7%

Radeon RX Vega M GH

3.583 +5.4%

Jetson AGX Orin 32 GB

3.4

FirePro S10000 Passive 12GB

3.337 -1.9%

Radeon HD 7950 Boost

3.249 -4.4%