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NVIDIA Jetson AGX Xavier GPU

NVIDIA Jetson AGX Xavier GPU

NVIDIA Jetson AGX Xavier: A Powerful Module for Developers and Professionals (2025 Analysis)

Introduction

The NVIDIA Jetson AGX Xavier is more than just a GPU; it is a complete computing platform designed for artificial intelligence tasks, autonomous systems, and robotics. Unlike desktop graphics cards, this compact module integrates a processor, graphics core, and specialized accelerators, offering a unique balance of performance and energy efficiency. In this article, we will explore who needs the AGX Xavier and why in 2025.

Architecture and Key Features

Architecture: At the core of Jetson AGX Xavier is the hybrid NVIDIA Carmel architecture (ARMv8.2) with an integrated Volta-based GPU. Despite the emergence of new generations (such as Orin), Xavier remains popular due to its optimization for edge computing.

Manufacturing Process: Built on TSMC's 12nm FinFET technology. While this is not the most modern process (latest NVIDIA cards use a 4nm process), this choice ensures stability and low cost for embedded systems.

Unique Features:

- 512 Volta CUDA Cores with INT8/FP16 support for accelerating AI algorithms.

- NVIDIA DLSS (only in software implementation): Unlike desktop RTX, there are no 4th generation hardware Tensor cores, but AI upscaling is possible through libraries.

- NVIDIA JetPack SDK: An ecosystem for developing software for robotics, including support for ROS, CUDA, and cuDNN.

Memory: Speed and Capacity

- Type: LPDDR4x (16 GB) with bandwidth of 137 GB/s.

- Features: Unlike gaming cards with GDDR6/X, this module uses energy-efficient memory, crucial for autonomous devices. The 16 GB capacity is sufficient for processing data from LiDARs and cameras in real time.

- Performance Impact: For computer vision tasks (e.g., object recognition in 4K video), high bandwidth reduces the risk of a “bottleneck.”

Gaming Performance: Not the Main Focus, But Possible

The Jetson AGX Xavier is not designed for AAA games, but it can be used in simulators and indie projects:

- Cyberpunk 2077 (1080p, Low): ~25-30 FPS through streaming from a PC (GeForce NOW).

- ROS Gazebo (3D Robot Simulation): 60 FPS at 1440p.

- Minecraft with RTX: 1080p/30 FPS (limited due to lack of RT cores).

Ray Tracing: Not supported in hardware. Rendering with ray tracing is only possible through software solutions (e.g., OptiX), which significantly reduces FPS.

Professional Tasks: Where Xavier Shines

- Video Editing: 4K/60fps processing in DaVinci Resolve using CUDA filters.

- 3D Modeling: In Blender, rendering a medium-complexity scene takes ~15 minutes compared to 5-7 minutes on an RTX 4070, but Xavier consumes three times less energy.

- Scientific Computing: Accelerating algorithms in Python (NumPy, TensorFlow) thanks to the 8-core CPU and CUDA. MLPerf test: 4500 images/sec in ResNet-50.

Power Consumption and Cooling

- TDP: 30W (Max-Q mode) or 50W (maximum performance).

- Cooling: A passive heat sink is included, but for prolonged workloads, cases with fans (e.g., from Seeed Studio) are recommended.

- Tip: When integrating into a drone or robot, avoid enclosed spaces without ventilation — overheating reduces performance by 20-30%.

Comparison with Competitors

- NVIDIA Jetson Orin Nano (2023): 40% faster in AI tasks but more expensive ($799 vs. $1099).

- AMD Ryzen V2000: Better in multi-threaded CPU tasks but weaker in CUDA optimization.

- Intel NUC 12 Extreme: More powerful in gaming but consumes 120W and is not suitable for embedded solutions.

Conclusion: Xavier excels in price balance ($999 in 2025) and specialization for edge AI.

Practical Tips

- Power Supply: 65W adapter (included), but for peripherals use sources with some reserve (90W).

- Compatibility: Ubuntu 22.04 LTS + JetPack 6.0. Avoid Windows — drivers are limited.

- Drivers: Update via NVIDIA SDK Manager — manual installation often breaks dependencies.

Pros and Cons

✅ Pros:

- Energy efficiency: 50W at a performance level of GTX 1660.

- Built-in support for AI frameworks.

- Compact size (100x87 mm).

❌ Cons:

- No HDMI/DisplayPort — image output via USB-C or Ethernet.

- Limited gaming compatibility.

- High price for non-professional use.

Final Conclusion: Who is AGX Xavier Suitable For?

This module is ideal for:

- Robotics Engineers creating autonomous drones or manipulators.

- AI Developers needing a portable setup for testing models.

- Industrial Designers working with 3D simulations on embedded systems.

If you are looking for a GPU for gaming or editing 8K video — consider the RTX 4060 or Apple M3 Pro. But for projects at the intersection of AI and the real world, Xavier remains an unmatched tool.

Basic

Label Name
NVIDIA
Platform
Integrated
Launch Date
October 2018
Model Name
Jetson AGX Xavier GPU
Generation
Tegra
Base Clock
854MHz
Boost Clock
1377MHz
Bus Interface
IGP
Transistors
9,000 million
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.
64
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.
32
Foundry
TSMC
Process Size
12 nm
Architecture
Volta

Memory Specifications

Memory Size
System Shared
Memory Type
System Shared
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.
System Shared
Memory Clock
SystemShared
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.
System Dependent

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.
22.03 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.
44.06 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.
2.820 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.
705.0 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.
1.382 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.
8
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.
512
L1 Cache
128 KB (per SM)
L2 Cache
512KB
TDP
30W
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.
1.2
OpenCL Version
1.2
OpenGL
4.6
DirectX
12 (12_1)
CUDA
7.2
Shader Model
6.4
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.
16

Benchmarks

FP32 (float)
Score
1.382 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS
Radeon Vega 9 Mobile
1.468 +6.2%
GeForce GTX 950M
1.41 +2%
Jetson AGX Xavier GPU
1.382
Quadro 7000
1.359 -1.7%
Radeon Vega 6
1.332 -3.6%