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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesBrian Benchoff’s March 14, 2017 Hackaday review presents NVIDIA’s Jetson TX2 as an embedded computing platform that combines a substantial CPU/GPU setup with configurable power modes. Its benchmark results and hardware details are useful as a historical snapshot—not as a current buying, support, or compatibility guide.
What the Jetson TX2 review covers
The review looks at both the TX2 module and its developer kit, describing their hardware, software, interfaces, power modes, and performance observations. Its central use case is edge computing: processing workloads such as computer vision and inference near the device, where performance must be balanced against power and physical constraints.
| # | Preview | Product | Price | |
|---|---|---|---|---|
| 1 |
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NVIDIA 945-82771-0000-000 Jetson TX2 Development Kit | $249.99 | Buy on Amazon |
| 2 |
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NVIDIA Jetson AGX Orin 64GB Developer Kit with Ethernet, USB, Display Port | $3,399.00 | Buy on Amazon |
| 3 |
|
NVIDIA Jetson Xavier Developer Kit (945-82972-0000-000) | $999.00 | Buy on Amazon |
Read Brian Benchoff’s Hackaday review, published March 14, 2017.
TX2 module and developer kit are different
The TX2 module is the compact computing unit; the developer kit pairs it with a larger, Mini-ITX-style carrier board. That distinction matters when judging a project’s footprint: the review’s description of the kit’s connectors applies to its carrier-board setup, not automatically to every custom carrier or module installation.
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- Developer Kit for the Jetson TX2 module. Includes Jetson TX2 module with NVIDIA Pascal GPU, ARM 128-bit CPUs, 8 GB LPDDR4, 32 GB eMMC, Wi-Fi and BT Ready
- NVIDIA Pascal Embedded module loaded with 8GB of memory and 58.4 GB/s of memory bandwidth
- Wi-Fi and BT Ready
Processing hardware
Benchoff describes the module as combining a dual-core NVIDIA Denver 2.0 CPU, a quad-core ARM Cortex-A57 CPU, and a Pascal GPU with 256 CUDA cores. The heterogeneous combination is intended to support workloads that can use CPU and GPU processing; the core count alone does not predict performance for a particular application.
Interfaces described in the review
The review lists the developer kit’s carrier board with full-size SD storage and SATA; USB 3.0 Type A and USB 2.0 Micro AB; Gigabit Ethernet; 802.11ac Wi-Fi and Bluetooth 4.1; PCIe x4; display and camera connectors; and M.2 Key E. It also describes I2C, I2S, SPI, UART, digital microphone, and JTAG connections.
These are review-era descriptions. Before designing hardware around any connector or bus, check the documentation for the exact developer-kit revision, carrier board, and TX2 module involved.
Rank #2
- The NVIDIA Jetson AGX Orin 64GB Developer Kit makes it easy to get started with Jetson Orin. Compact size, lots of connectors, and up to 275 TOPS of AI performance make this developer kit perfect for prototyping advanced AI-powered robots and other autonomous machines.
- The developer kit includes a Jetson AGX Orin 64GB module, and can emulate all the Jetson Orin modules. It supports multiple concurrent AI application pipelines with the NVIDIA Ampere GPU architecture, next-generation deep learning and vision accelerators, high-speed IO and fast memory bandwidth. Now you can develop solutions using your largest and most complex AI models to solve problems such as natural language understanding, 3D perception, and multi-sensor fusion.
- Jetson runs the NVIDIA AI software stack, and use-case specific application frameworks are available, including Isaac for robotics, DeepStream for vision AI, and Riva for conversational AI. You can save significant time with NVIDIA Omniverse Replicator for synthetic data generation (SDG), and by using NVIDIA TAO toolkit to fine-tune pretrained AI models from the NGC catalog.
- Jetson ecosystem partners offer additional AI and system software, developer tools, and custom software development. They can also help with cameras and other sensors, as well as carrier boards and design services for your product.
- With the computing capability of more than 8 Jetson AGX Xavier systems in a developer kit that integrates the latest NVIDIA GPU technology with the world’s most advanced deep learning software stack, you’ll have the flexibility to create tomorrow’s AI solution as well as today’s.
Power modes and reported consumption
The review describes two operating modes: Max Q and Max P. Benchoff measured about 7.5 W in Max Q and about 15 W in Max P. Those are author-reported, review-era measurements, not guaranteed whole-system consumption figures: actual draw depends on the configuration and workload, among other factors.
The practical trade-off is that a lower-power mode may better suit a constrained thermal or energy budget, while a higher-power mode allows a different operating point. The review’s figures do not establish the performance or power behavior of every application in either mode.
What the performance comparisons do—and do not—show
The review reports two different comparisons, based on different evidence. They should not be combined into a general claim that the TX2 is a fixed multiple faster than another board.
Rank #3
- 512-Core Volta GPU with Tensor Cores
- 8-Core ARM 64-Bit CPU
- 16 GB 256-Bit LPDDR4 memory
| Comparison | What the review reports | How to interpret it |
|---|---|---|
| UnixBench CPU tests versus Raspberry Pi 3 Model B | About four times the performance in the author’s tests. | A result for the review’s CPU benchmark and setup, not a prediction for all workloads. |
| GoogleNet inference versus Jetson TX1 | Nearly twice the performance, based on NVIDIA benchmark results cited in the review. | A vendor-reported comparison for that inference workload, as relayed in the 2017 article. |
Neither comparison is a current, independent ranking across embedded systems. For a real project, performance depends on the software, model, input, optimization, and operating conditions being tested.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where the TX2 fits in an embedded project
The review’s strongest case for the TX2 is local processing where a project needs more compute capability than a small board may provide, but cannot treat a desktop-class computer’s power draw and size as acceptable. Examples include robotics, computer vision, and local inference. The right choice still depends on the actual workload and the rest of the system.
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- Compute: Determine whether the application benefits from the TX2’s CPU/GPU combination, then test the specific workload rather than extrapolating from the review’s benchmarks.
- Power and thermal limits: Compare the project’s supply and cooling budget with measurements from the intended configuration; the review’s mode figures are not universal system ratings.
- Physical size: Account separately for the compact module and the larger developer-kit carrier board.
- I/O: Verify the exact camera, display, storage, networking, and expansion interfaces on the carrier board you plan to use.
- Software and availability: Confirm that the required software, components, and support are available for the exact hardware and project timeline.
What to verify before relying on TX2 today
The review is from 2017, so it cannot establish present-day sales status, support lifecycle, software compatibility, or component availability. NVIDIA’s Jetson TX2 Module product page is an official reference, but its accessible content does not establish those current status details. Check directly with NVIDIA and the relevant hardware or software provider before committing a design or purchase.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
