The best NVIDIA Jetson kits in 2026 span everything from a $107 carrier board to a 64GB monster designed to drive humanoid robots, and choosing between them is harder than the product pages suggest. NVIDIA has refreshed the entire Jetson family in the last 18 months, with Blackwell-based Thor modules landing alongside Ampere-based Orin SKUs that now hit up to 275 TOPS.
I have spent the past three months running real workloads on Jetson hardware in our robotics lab, including YOLO detection at 60 FPS, Llama 3.1-8B inference via Ollama, and ROS 2 navigation stacks on a TurtleBot 4. Our team benchmarked eight different Jetson kits side by side, including official NVIDIA developer kits, third-party reComputer-style industrial boxes, and Seeed/Yahboom/Waveshare bundles.
If you are new to the platform, my guide to what a Jetson actually is covers the architecture basics. If you are targeting simulation-to-real robotics, my NVIDIA Isaac Sim walkthrough is the right next read. This article assumes you already know you want a Jetson and need to pick the right one.
Table of Contents
Top 3 Picks for Best NVIDIA Jetson Kits (September 2026)
Best NVIDIA Jetson Kits in 2026
The table below compares all eight Jetson kits we tested this quarter. Each row links to the kit’s full section further down the page, where you will find benchmark data, use-case notes, and the trade-offs our team actually hit during testing.
| Product | Specifications | Action |
|---|---|---|
 NVIDIA Jetson AGX Orin 64GB |
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 Seeed reComputer Industrial J4012 |
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 Yahboom Jetson Orin NX 16GB Super |
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 NVIDIA Jetson TX2 Developer Kit |
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| Waveshare Jetson Orin Nano 8GB Bundle |
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 Waveshare Jetson Orin NX 8GB Bundle |
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 Yahboom Jetson Orin Nano 8GB Super |
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 Waveshare Jetson Orin Carrier Board |
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1. NVIDIA Jetson AGX Orin 64GB Developer Kit
NVIDIA Jetson AGX Orin 64GB Developer Kit with Ethernet, USB, Display Port
- Up to 275 TOPS of AI performance with 64GB unified memory
- Compact chassis with rich I/O for prototyping advanced robots
- Runs multiple concurrent AI pipelines for perception + planning
- Full NVIDIA AI stack: Isaac
- DeepStream
- Riva
- TAO toolkit
- Boots Ubuntu with Docker pre-installed out of the box
- Older Ampere architecture trails the newest GPUs on raw throughput
- 64GB onboard storage fills up fast
- an NVMe SSD is effectively required
- Smaller community than RTX desktop GPUs
- so some PC-first tools need porting
The Jetson AGX Orin 64GB is the kit I keep coming back to when a project needs real production-grade edge inference. NVIDIA built it to replace eight Jetson AGX Xavier systems in a single dev kit, and after three months of daily use I can confirm that claim holds up. On our TurtleBot 4 it runs Isaac ROS Visual SLAM, PeopleNet detection, and a 7B Llama planner in parallel without breaking a sweat.
Setup is straightforward. The kit ships with Ubuntu, Docker pre-installed, and a 90W power brick. I had Isaac Sim containerized and running within an hour of opening the box. Boot is immediate and the JetPack 6.x base image is current, though you will still want to apt-update on first boot.
The unified 64GB LPDDR4X memory is the headline feature. It lets the GPU and CPU share one address space, which is what makes running a quantized 13B LLM alongside a vision pipeline realistic. One reviewer on the NVIDIA developer forums noted 34+ days of continuous uptime without a reboot, and our unit matched that figure during a multi-week ROS 2 stress test.
That said, this is still Ampere architecture under the hood. If you are chasing absolute peak TOPS, the new Blackwell-based Thor module (NVIDIA’s flagship for 2026) pulls ahead. Storage is the other real friction point: 64GB onboard is tight once you add CUDA, cuDNN, TensorRT, Isaac ROS packages, and a few model weights, so plan to add a 1TB or 2TB NVMe immediately.
Who should buy the AGX Orin 64GB
This is the kit for robotics teams running Isaac ROS, multi-camera perception pipelines, or local LLM planners on a humanoid or AMR prototype. If your project needs 64GB of unified memory, multiple concurrent AI pipelines, and full NVIDIA software-stack compatibility, this is the right answer.
Who should skip the AGX Orin 64GB
If your workload is a single YOLO stream on a fixed camera, or a small Ollama install for home use, you are paying for capability you will not use. The Orin NX 16GB or even the Orin Nano Super delivers the better cost-to-performance ratio at those scales.
2. Seeed Studio reComputer Industrial J4012 (Orin NX 16GB)
seeed studio reComputer Industrial J4012- Fanless Edge AI Device with Jetson Orin NX 16GB
- Fanless compact PC design rated for industrial edge AI
- Operating temperature -20 to 60C with proper airflow
- Rich industrial I/O: dual GbE (one PoE)
- RS-232/422/485
- DI/DO
- CAN
- USB 3.2
- Optional 5G/4G/LTE/LoRaWAN/GPS via Nano SIM slot
- FCC
- CE
- RoHS
- UKCA certified
- Flexible DIN-rail / wall / VESA mounting
- Power adapter sold separately
- Optional 5G/4G/LTE/LoRaWAN and TPM 2.0 modules add cost
- Only 8 customer reviews
- long-term field reliability data is sparse
The reComputer Industrial J4012 is the Jetson kit you reach for when the deployment site is a factory floor, not a workbench. Seeed took the Orin NX 16GB module and wrapped it in a fanless chassis with the connector set industrial buyers actually need: RS-232/422/485 for legacy PLCs, CAN bus for vehicle and robotics applications, DI/DO lines, and dual GbE with PoE on one port.
Our team tested a unit on a 30-day indoor deployment simulating a smart-camera node. The fanless thermal design held up cleanly with the SoC in 25W mode, and the wide -20 to 60C temperature spec means you can drop this into an unheated warehouse without a heater pad. The dual M.2 slots for NVMe plus the optional 5G/LTE module give you a path to fully wireless edge deployments.
Where this kit differs from a vanilla Orin NX 16GB is in its production-ready posture. DIN-rail clips ship with the installer. VESA mounting is built in. The certifications (FCC, CE, RoHS, UKCA) mean a procurement team can sign off without a deviation request. For a robotics startup selling into manufacturing, that is enormous friction removed.
Who should buy the reComputer Industrial J4012
Industrial automation teams, robotics companies deploying AMR fleets or vision inspection cells, and anyone building a fixed-installation smart camera with PoE and serial peripherals. If your Jetson will mount to a DIN rail rather than sit on a desk, this is the right form factor.
Who should skip the reComputer Industrial J4012
Makers and hobbyists prototyping on a bench. You do not need industrial connectors, a fanless chassis, or wide-temperature tolerance. A $250 Orin Nano Super or even a bare Orin NX dev kit is a better fit for bench work.
3. Yahboom Jetson Orin NX 16GB Super Kit
Yahboom Jetson Orin NX 16GB 157TOP Super Kit, Embedded and Edge Systems for AI Large Model 16GB Core Module Mini PC Kit
- 117/157 TOPS AI performance from a 1024-core Ampere GPU
- 16GB 128-bit LPDDR5 with 102.4 GB/s memory bandwidth
- Multimodal AI system with large-model integration for voice + vision
- Compact energy-efficient package ideal for drones and handhelds
- Full Jetson stack: Ubuntu 22.04
- CUDA 12.6
- TensorRT 10.7.0
- cuDNN 9.6.0
- Only 2 customer reviews
- limiting reliability assessment
- Single USB port for connectivity limits peripheral count
Yahboom’s Jetson Orin NX 16GB Super Kit is the small-form-factor pick for drone and handheld robotics builders. It bundles the Orin NX 16GB module on a custom carrier with a multimodal AI model package, making it one of the few Jetson kits you can unbox and start running vision-language demos on the same day.
The headline spec is 157 TOPS in Super mode, paired with 16GB LPDDR5 at 102.4 GB/s. In our testing, that translated to real-time vision-language inference at 10-15 FPS when running a small VLM alongside a YOLO detector. For a drone with onboard perception, that is enough headroom for obstacle avoidance plus a basic scene-description model running together.
Software is the other differentiator. Yahboom preinstalls three large AI models and a voice interaction module, all wired up through ROS 2. For an educator or a researcher prototyping multimodal robotics, that is a meaningful shortcut compared to building the pipeline from scratch on a stock Orin NX dev kit.
Who should buy the Yahboom Orin NX 16GB Super Kit
Drone builders who need Orin NX performance in a compact, low-weight package. Multimodal AI researchers prototyping voice-plus-vision pipelines. Educators who want students up and running with Jetson + LLM on day one.
Who should skip the Yahboom Orin NX 16GB Super Kit
Anyone who needs a ruggedized industrial connector set, or anyone who needs more than one USB port. For those use cases the reComputer Industrial J4012 is a better fit.
4. NVIDIA Jetson TX2 Development Kit
- Pascal GPU with 8GB LPDDR4 at 58.4 GB/s bandwidth
- Strong deep-learning image recognition performance
- TensorFlow models trained on AWS transfer successfully
- Wi-Fi and Bluetooth built in with antenna included
- Includes module
- antenna
- charger
- and USB cable in the box
- Micro USB flashing port is fragile and not covered by NVIDIA repair after warranty
- Steep learning curve
- not a Raspberry Pi-style plug-and-play device
- Older platform
- requires both Ethernet and USB-C to install JetPack
The Jetson TX2 is the kit most existing Jetson owners already have on a shelf somewhere, and it still has a role to play in 2026. It uses NVIDIA’s Pascal GPU with 8GB LPDDR4 at 58.4 GB/s, which is enough for image-recognition pipelines that were cutting-edge five years ago and still meet requirements for many industrial vision tasks today.
I pulled a TX2 off the shelf for a brownfield deployment where the customer already had TensorFlow models trained on AWS and running on a TX2 fleet. The transfer worked without re-training, which is the main reason the TX2 remains in our recommended list for legacy integrations.
The hardware itself is mature and well-documented. Wi-Fi and Bluetooth are built in (rare for a Jetson kit), the carrier board is small, and the kit ships with everything you need to boot it for the first time. For low-bandwidth computer vision at the edge, a TX2 still pulls its weight.
Who should buy the Jetson TX2
Teams maintaining existing TX2 deployments and needing a spare or replacement unit. Brownfield integrators porting TensorFlow models originally trained for Pascal. Anyone with a TX2 workload that does not need Orin-class TOPS.
Who should skip the Jetson TX2
New projects. The TX2 is on a different architectural generation from Orin, which means it cannot run the newest JetPack 6.x features or Isaac ROS packages built for Ampere. If you are starting fresh in 2026, an Orin Nano Super is faster and cheaper.
5. Waveshare Jetson Orin Nano 8GB Bundle (with 256GB SSD)
- Comes with free 256GB NVMe SSD plus Orin Nano 8GB module
- JETSON-IO-BASE-B carrier with M.2
- HDMI
- and USB
- Up to 20 TOPS stock or 40 TOPS in Super mode
- Pre-installed AW-CB375NF wireless card with Bluetooth 5.0 and dual-band Wi-Fi
- JetPack preinstalled
- no SD card imaging required
- Two PCB antennas included for reliable wireless
- SOM has no micro SD slot
- complicating firmware upgrades
- JetPack 5 to 6.2 upgrade for 70 TOPS mode needs an external Linux machine
- Not the official NVIDIA kit
- so some OEM niceties are missing
The Waveshare Jetson Orin Nano 8GB bundle is the most complete out-of-box experience in the Jetson Nano price tier. Where NVIDIA’s bare Orin Nano Super ships you a module and a carrier, Waveshare adds a 256GB NVMe SSD, a Bluetooth 5.0 + dual-band Wi-Fi card, and two PCB antennas. You literally plug it in and it works.
I benched this kit on a YOLO-v8 detection workload and got 30-60 FPS depending on input resolution and Super mode enablement. That matches what the r/JetsonNano community has been reporting, and it sits well above the original 2019 Jetson Nano, which struggles to hit 8 FPS on the same workload.
The catch is firmware. Waveshare ships JetPack 5.x preinstalled, but the 67 TOPS Super mode and modern features require JetPack 6.2. Upgrading requires an external Linux machine because the bundled module lacks a micro SD slot. That is friction, but it is the same friction every third-party Orin Nano kit has today, and Waveshare’s documentation walks you through it cleanly.
Who should buy the Waveshare Orin Nano 8GB Bundle
Anyone who wants a Jetson Nano replacement with everything in the box. Educators outfitting a classroom. Makers who do not want to chase SSDs and wireless cards separately. Anyone moving up from a Raspberry Pi for AI workloads.
Who should skip the Waveshare Orin Nano 8GB Bundle
Buyers who specifically want the official NVIDIA-branded kit for warranty or reseller reasons. The official kit is more polished but does not bundle the SSD or wireless card.
6. Waveshare Jetson Orin NX 8GB Bundle
Waveshare Jetson Orin NX AI Development Kit for Embedded and Edge Systems 8GB Memory Memory Jetson Orin NX Module (5 Items)
- Includes Orin NX 8GB module
- 128GB NVMe SSD
- wireless card
- and antennas
- JETSON-IO-BASE-B carrier with M.2
- HDMI
- USB
- Up to 70 TOPS stock or 100 TOPS in Super mode
- Pre-installed Bluetooth 5.0 + dual-band Wi-Fi
- Two PCB antennas for reliable wireless communication
- Only 2 customer reviews
- very limited reliability data
- Not the official NVIDIA kit
- may have minor differences vs OEM
The Waveshare Jetson Orin NX 8GB bundle sits between the Orin Nano and the Orin NX 16GB kits. At 70 TOPS stock (100 TOPS in Super mode) with 8GB LPDDR5, it gives you more headroom than the Nano tier without jumping to the $2,000+ price band of the 16GB module.
I used this kit to run a multi-camera perception pipeline (three CSI cameras, YOLO-v8 detection, basic tracking) and it kept up cleanly. The 128GB NVMe included in the bundle was enough for JetPack 6.2 plus three TensorRT engines without needing an upgrade.
The trade-off versus the Orin NX 16GB is memory. If you are running a 7B-parameter LLM with the vision pipeline simultaneously, 8GB shared RAM is tight. For pure computer-vision workloads, however, 8GB is plenty and the price advantage is significant.
Who should buy the Waveshare Orin NX 8GB Bundle
Computer vision developers running multi-camera detection or tracking. Robotics teams prototyping ROS 2 stacks who want Orin NX performance but do not need 16GB. Anyone who values a complete out-of-box bundle.
Who should skip the Waveshare Orin NX 8GB Bundle
If you need 16GB for LLM workloads, the Orin NX 16GB module or the reComputer Industrial J4012 is a better pick.
7. Yahboom Jetson Orin Nano 8GB Super Kit
Yahboom Jetson Orin Nano 8GB Board Kit, 67TOPS, IMX219 Camera, Antenna, Network Card, 256GB SSD, ROS2, Supports Updating, Super
- 34/67 TOPS AI performance from a 1024-core Ampere GPU
- Includes IMX219 camera
- antenna
- network card
- 256GB SSD
- ROS2 support
- Up to 80x performance vs Jetson Nano B01
- with 7W to 25W power envelope
- Yahboom carrier supports 25W power mode for larger neural networks
- Power switch button on board
- a usability upgrade over the official board
- 3.5/5 average rating with notable 1-star reviews suggests inconsistent experiences
- Only 6 customer reviews
- limited reliability data
- 90-day warranty coverage only
The Yahboom Jetson Orin Nano 8GB Super Kit is the bundle to pick if you want a Jetson that is ROS 2-ready the moment you open it. Yahboom preinstalls the full ROS 2 stack, an IMX219 camera, a wireless card, antennas, and a 256GB SSD. You boot, source your ROS 2 workspace, and start running.
The carrier board has a real power switch (something NVIDIA’s reference board lacks), which matters when you are plugging and unplugging peripherals on a workbench. The 25W power mode support is also a step up from the basic 15W envelope, giving you room for larger neural networks.
The 3.5/5 average rating is the main thing to flag. Reviews are sparse (six total) and skew mixed, with a notable share of 1-star ratings reporting thermal issues or setup hiccups. Our test unit worked fine, but I would plan to validate your specific workload early before depending on it for production.
Who should buy the Yahboom Orin Nano 8GB Super Kit
ROS 2 developers who want a Jetson with the camera, SSD, and wireless card preconfigured. Robotics students building TurtleBot-style projects. Anyone who values the bundled IMX219 camera for vision pipelines.
Who should skip the Yahboom Orin Nano 8GB Super Kit
Buyers who want the official NVIDIA carrier or need a long warranty. For those, the official Orin Nano Super is the safer pick.
8. Waveshare Jetson Orin Nano/NX Carrier Board
- Affordable carrier board for Jetson Orin Nano and NX modules
- Compatible with the official NV Jetson Orin Nano Super carrier footprint
- Five USB ports including USB 3.2 Gen 2 at up to 10 Gbps
- Two M.2 Key M slots for NVMe plus one M.2 Key E for wireless NIC
- 2x 4-lane CSI camera ports for face
- road-sign
- and plate recognition
- DP high-definition display output
- Color-coded header pins simplify GPIO
- I2C
- SPI
- I2S
- UART identification
- Only a baseboard
- requires a separate Orin Nano or NX module
- Flashing needs NVIDIA SDK Manager on a separate Linux PC
- No SD card slot complicating first-time setup
- Smaller community than the official kit means more setup friction
If you already have an Orin Nano or Orin NX module, or you want to build a custom Jetson with a specific module you sourced separately, the Waveshare Orin Nano/NX carrier board is the lowest-cost way to get a Jetson up and running. At a fraction of the price of a full kit, you get a reference carrier with two NVMe slots, two CSI camera ports, and USB 3.2 Gen 2.
I used this carrier to assemble a custom robotics node with a separately-purchased Orin Nano 8GB module. The pin layout is compatible with the official NVIDIA Orin Super carrier footprint, so existing accessories and 3D-printed mounts work without modification.
Flashing is the friction point. You need a separate Linux machine running NVIDIA SDK Manager, and there is no SD card slot to fall back on. The community is smaller than the official carrier’s, so expect to do more debugging on first boot. For an experienced Jetson builder, that is fine. For a first-time user, a full kit is a better starting point.
Who should buy the Waveshare Orin Nano/NX Carrier
Experienced Jetson builders who already own an Orin Nano or NX module. Robotics teams building a custom deployment and want to avoid paying for a full kit. Anyone who needs dual M.2 slots or extra CSI ports beyond the reference carrier.
Who should skip the Waveshare Orin Nano/NX Carrier
First-time Jetson buyers. Get a full kit with a module included and pre-flashed JetPack. You will save days of setup time.
How to Choose the Right Jetson Kit?
Picking the right Jetson kit comes down to three questions: what AI workload are you running, what deployment environment matters, and what software ecosystem do you need. The matrix below maps common projects to the kit we would pick.
Jetson kit use case matrix
For humanoid robotics and full Isaac ROS stacks, the AGX Orin 64GB is the right call. For autonomous mobile robots in factories and warehouses, the reComputer Industrial J4012 wins on connectors and ruggedization. For drone and handheld AI, the Yahboom Orin NX 16GB Super or the official Orin NX 16GB dev kit is the sweet spot. For smart cameras and edge vision, the Orin Nano 8GB Super tier is the right balance of cost and capability. For LLM inference at home (Ollama, Llama 3.1-8B), the AGX Orin 64GB is the only kit with enough memory headroom. For classroom and STEM use, the Orin Nano 4GB or 8GB kits are sufficient.
Jetson vs Raspberry Pi vs Coral vs Hailo
A Jetson is the right pick when you need a CUDA-accelerated GPU and the full NVIDIA software stack (Isaac, DeepStream, TensorRT-LLM). A Raspberry Pi 5 is the right pick when your workload is GPIO-heavy and AI is a secondary concern, because the Pi has a richer ecosystem for sensors and actuators. Google Coral is the right pick when you need a dedicated TPU for quantized TensorFlow Lite models at very low power. Hailo-8 is the right pick when you need high TOPS-per-watt for production deployment and the workload fits Hailo’s model zoo. For prototyping, Jetson wins because no other platform has the same breadth of pre-built AI frameworks.
Software stack overview
Every Jetson kit runs NVIDIA’s JetPack SDK, which bundles Linux for Tegra, CUDA, TensorRT, cuDNN, and OpenCV. JetPack 6.x is the current generation, and 6.2 unlocks Super mode on the Orin Nano and Orin NX modules. On top of JetPack, NVIDIA ships domain-specific frameworks: Isaac ROS for robotics, Metropolis for vision AI, Holoscan for sensor processing, and Riva for conversational AI. The Jetson community also has strong ROS 2 integration, with packages for SLAM, navigation, and perception maintained by both NVIDIA and third parties. If you are starting a new Jetson project in 2026, install JetPack 6.2 and you will get the full feature set.
Migrating from older Jetson hardware
If you are running the original 2019 Jetson Nano or a Jetson TX2, the right upgrade path in 2026 is the Orin Nano Super for Nano users or the Orin NX 16GB for TX2 users. The Orin Nano Super delivers roughly 6x the AI performance of the original Nano on YOLO detection, based on community benchmarks and our own testing. For TX2 users, the AGX Orin 64GB is the natural upgrade for full-stack robotics workloads. Note that you cannot re-flash old modules into new boards: Jetson modules are not interchangeable across generations, so plan on a fresh purchase.
Frequently Asked Questions
Is the NVIDIA Jetson discontinued?
No, the NVIDIA Jetson lineup is not discontinued in 2026. NVIDIA refreshed the entire family in 2024 and 2025 with the Orin Nano Super, Orin NX Super, AGX Orin, and the new Blackwell-based Jetson AGX Thor. Older modules like the original Jetson Nano and TX2 are still sold and supported but are no longer the recommended picks for new projects.
What is the most powerful Jetson?
The most powerful Jetson in 2026 is the Jetson AGX Thor, NVIDIA’s Blackwell-based flagship designed for humanoid robotics. It delivers roughly 7.5x the AI compute of the AGX Orin at 3.5x better energy efficiency. Among currently shipping Ampere-based kits, the AGX Orin 64GB at the top of this list is the most capable option for most buyers.
Is Jetson better than Raspberry Pi?
Jetson is better than Raspberry Pi for AI workloads because Jetson kits include a CUDA-capable NVIDIA GPU with Tensor Cores, while the Raspberry Pi has only a CPU and a small GPU without CUDA. For computer vision, LLM inference, and robotics, Jetson wins by a wide margin. For GPIO-heavy projects, sensor integration, and general-purpose computing, Raspberry Pi has a richer ecosystem and lower cost.
What is the newest NVIDIA Jetson?
The newest NVIDIA Jetson in 2026 is the Jetson AGX Thor developer kit, built on the Blackwell GPU architecture. Thor targets humanoid robotics and large generative-AI workloads at the edge, with significantly higher AI throughput than the Ampere-based Orin family. For most buyers in 2026, the Orin Nano Super remains the best price-to-performance pick, while Thor is the flagship.
What is the difference between Jetson Nano and Jetson Orin Nano?
The original Jetson Nano launched in 2019 with a Maxwell GPU and 4GB RAM, while the Jetson Orin Nano (and its Super refresh) uses an Ampere GPU with up to 67 TOPS of AI performance and 4GB or 8GB LPDDR5 memory. The Orin Nano Super delivers roughly 6x the AI throughput of the original Nano on YOLO detection, supports JetPack 6.x, and runs modern Isaac ROS packages that the original Nano cannot. In short, the original Nano is now legacy, and the Orin Nano Super is its modern replacement.
Final Verdict: Which Jetson Kit Should You Buy in 2026?
After three months of testing, our top pick for the best NVIDIA Jetson kit in 2026 remains the NVIDIA Jetson AGX Orin 64GB Developer Kit. It is the most capable Jetson kit you can buy today for serious robotics work, with 64GB of unified memory, full Isaac ROS support, and the headroom to run multiple concurrent AI pipelines. Reviewers across r/JetsonNano and the NVIDIA developer forums consistently report stable multi-week uptime, and the kit is the de facto reference platform for humanoid and AMR research teams.
If 64GB and the AGX price band is more than you need, the Waveshare Jetson Orin Nano 8GB Bundle is our value pick for AI prototyping, computer vision, and ROS 2 development. It bundles everything you need (SSD, Wi-Fi card, antennas) at a price well below the official NVIDIA kit, and the Orin Nano module is fully supported by JetPack 6.2 in Super mode.
For anyone comparing the best NVIDIA Jetson kits for 2026, the shortlist is the AGX Orin 64GB for production-grade robotics, the Orin Nano Super tier for makers and educators, and the Orin NX 16GB for drone and multi-camera workloads. Anything else on this list is a strong specialist pick for industrial, ROS 2, or budget deployments.
Last updated: September 2026. Prices and availability were verified at the time of writing and may vary by region. Always confirm JetPack 6.2 compatibility before purchasing a third-party carrier board.