NVIDIA Jetson Orin Super Nano Deployment Kit (Made in India): Everything in One Box, Ready to Deploy
by Gaurav Sarraf
The NVIDIA Jetson Orin Nano Super Developer Kit, priced at $249, is the most popular entry point for edge AI development at Think Robotics. But it is a prototyping tool. It ships with a reference carrier board, a power supply, and the module itself. Storage, wireless, and a production-grade carrier board are all separate decisions.
The NVIDIA Jetson Orin Super Nano Deployment Kit, assembled in India, solves exactly that gap. It is a complete, ready-to-deploy edge AI system built around the Jetson Orin Nano module, the Waveshare JETSON-ORIN-IO-BASE carrier board, a preinstalled NVMe SSD, a wireless network card, and all the cables you need to get started the same day the box arrives.
At Think Robotics, this product ranked fifth by revenue in the last 90 days of sales data. Teams ordering it are not experimenting. They are deploying.
Made in India: This kit is assembled and configured locally. NVIDIA modules sourced through Think Robotics carry a three-year warranty on the module itself. The included power adapter uses an Indian three-pin plug, so no adapter is needed on day one.
What Is in the Box
This kit is offered in two compute configurations: 4GB and 8GB, based on the Jetson Orin Nano module variant selected. All of the following are included in both variants:
Jetson Orin Nano Module in either 4GB or 8GB configuration, mounted and ready
NVMe SSD preinstalled: 128GB for the 4GB variant, 256GB for the 8GB variant. The SSD is already assembled on the board and loaded with the operating system
AW-CB375NF dual-band wireless network card preinstalled in the M.2 Key E slot, with support for 2.4 GHz and 5 GHz Wi-Fi and Bluetooth 5.0. Two PCB antennas included
Cooling fan for sustained AI inference workloads
Power adapter with Indian three-pin plug, USB cable, and Ethernet cable. Everything needed for the first boot is included
AI Performance: 4GB vs 8GB
Both variants use the NVIDIA Ampere GPU architecture with CUDA support and TensorRT optimization, and both run the full NVIDIA JetPack software stack. The 8GB kit gains access to Super Mode after upgrading to JetPack 6.2, which raises its ceiling significantly without any hardware change.
Super Mode requires a flash: Unlocking 67 TOPS on the 8GB variant requires upgrading to JetPack 6.2 using a host Ubuntu machine and Waveshare's flashing scripts. The upgrade is documented in Waveshare's wiki and takes approximately 30 to 45 minutes. It cannot be done through NVIDIA SDK Manager for this carrier board variant.
The Carrier Board: What It Provides
The Waveshare JETSON-ORIN-IO-BASE is the production carrier board included in this kit. It provides nearly the same interface set as the official NVIDIA reference carrier board, with the added advantage of two M.2 Key M slots instead of one. That means a second NVMe SSD can be added later without occupying the only storage slot.
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4× USB Type-A
USB 3.2 Gen 2, up to 10 Gbps each
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1× USB Type-C
For system flashing and data transfer
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Gigabit Ethernet (RJ45)
For ROS 2 comms and network connectivity
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DisplayPort Output
4K display support
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40-Pin GPIO Header
I2C, SPI, UART, GPIO : same layout as Raspberry Pi
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2× M.2 Key M Slots
NVMe SSD expansion. One slot used by preinstalled SSD
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1× M.2 Key E Slot
Used by preinstalled AW-CB375NF wireless NIC
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2× CSI Camera Ports
4-lane and 2-lane for AI vision applications
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Wide DC Input
9V to 19V : compatible with industrial power rails
What This Kit Is Designed For
The Deployment Kit is intended for teams who have moved past prototyping and need to ship AI at the edge. The preinstalled NVMe storage, configured wireless card, wide-range DC input, and Indian power adapter remove every sourcing step that would otherwise delay the first deployment.
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AI Surveillance and Video Analytics
Handles multi-stream RTSP video feeds with DeepStream. Runs YOLO-class object detection at 30 to 60 FPS depending on model size. CSI camera ports allow direct camera integration without USB.
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Industrial Inspection and Quality Control
Continuous defect-detection pipelines at low power. The 9V to 19V wide input range makes the carrier board compatible with standard industrial power rails without an intermediate adapter.
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Robotics Systems
GPIO header for servo control, USB ports for LiDAR and depth cameras, Ethernet for ROS 2 communication. ROS 2 Humble runs on Ubuntu 22.04 under JetPack 6.x.
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Local LLM Inference (8GB)
With Super Mode enabled, the 8GB kit can run quantized models up to 3B parameters at practical speeds using Ollama with JetPack 6.2. Suitable for field-deployed systems that cannot rely on cloud connectivity.
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Healthcare and City AI
Powered by NVIDIA Metropolis for vision AI and Riva for speech AI, both available through JetPack and running on the Orin Nano hardware.
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IoT Gateways and Edge Sensors
The 4GB variant suits lighter edge deployments: vision cameras, basic object detection, and sensor processing at low power draw (7W to 25W configurable).
Deployment Kit vs Developer Kit
The Orin Nano Super Developer Kit at $249 is the right starting point for software development, prototyping, and research. It uses the official NVIDIA reference carrier board, which is the correct environment for validating software before production. The Deployment Kit is the right choice when you need to ship a working system rather than continue developing one.
For prototyping
Developer Kit ($249)
Storage
Not included (MicroSD or NVMe separately)
Wireless
Not included
Power Adapter
International plug
Carrier Board
NVIDIA reference board
M.2 Key M Slots
1 slot
Warranty
Standard
Best for
Development, prototyping, R&D
For deployment
Deployment Kit (India)
Storage
128GB or 256GB NVMe, preinstalled
Wireless
AW-CB375NF preinstalled (Wi-Fi + BT 5.0)
Power Adapter
Indian three-pin plug included
Carrier Board
Waveshare JETSON-ORIN-IO-BASE
M.2 Key M Slots
2 slots (second free for expansion)
Warranty
3-year module warranty
Best for
Field deployment, fleet orders
Higher tier
Edge AI Device (J4012)
Storage
240GB NVMe preloaded
Wireless
Included
Power Adapter
Included
Module
Orin NX 16GB
AI Performance
Up to 157 TOPS (Super Mode)
Enclosure
3D-printed carbon-fiber
Best for
Multi-model, high-TOPS deployments
When to upgrade to the J4012: If your application requires more than 67 TOPS or needs the Orin NX's larger memory bandwidth for multi-model pipelines, the ThinkRobotics Edge AI Device with Jetson Orin NX 16GB is the correct choice. For single-model deployments, IP camera systems, and cost-sensitive fleet deployments, the Orin Nano Deployment Kit offers a lower per-unit cost while still delivering a production-ready system.
Software and JetPack
The kit ships with JetPack preloaded on the NVMe SSD. JetPack includes Ubuntu Linux, CUDA, cuDNN, TensorRT, DeepStream, and VPI. The NVIDIA software platforms available include Isaac for robotics, Riva for AI speech, and Metropolis for video analytics.
For the 8GB variant, upgrading to JetPack 6.2 unlocks Super Mode and raises AI performance from 40 TOPS to 67 TOPS. This upgrade requires a host Ubuntu machine and the flashing script provided by Waveshare. The process is documented in Waveshare's wiki and takes approximately 30 to 45 minutes.
ROS 2 Humble runs on Ubuntu 22.04 under JetPack 6.x and integrates well with the board's GPIO, USB, and Ethernet interfaces for robotics deployments.
Ubuntu Linux (JetPack)CUDA + cuDNN + TensorRTDeepStream for video analyticsIsaac for roboticsRiva for AI speechMetropolis for vision AIROS 2 Humble compatibleOllama / Local LLM (8GB)
What Users Are Saying
Feedback from the Think Robotics product page, the NVIDIA Developer Forums, Balena Forums, and developer community sources reflects a practical picture of real-world deployment experience with this kit and the Waveshare carrier board ecosystem.
The kit arrived in a plastic cover box with all contents intact. The included power adapter is good quality and comes with a three-pin Indian plug, so no adapter is needed.
PositiveVerified buyer, ThinkRobotics
I purchased a Jetson Orin Nano dev kit in August 2024 and have been using it ever since for video processing and model inferencing. I am now looking to buy 3 more Nano Supers after comparing the specs and the reduced price.
PositiveNVIDIA Developer Forums, Oct 2025
The Jetson Orin Nano Super is a compact computing powerhouse that brings sophisticated AI capabilities to edge devices. It blends performance with affordability and solid integration options, making it ideal for prototyping and commercial product development.
PositiveStorageReview, Feb 2025
Coming from RPi hardware, I needed NPU / CUDA for a project. I bundled the AW-CB375NF with the Waveshare carrier and wanted to confirm out-of-the-box BalenaOS support. Rockchip boards were mediocre in terms of software : the Jetson ecosystem is more mature.
Developer NoteBalena Forums, Feb 2024
Flashing the Waveshare carrier board for Super Mode requires a script, not SDK Manager. The process works, but needs a host Ubuntu machine and takes about 30 to 45 minutes. Plan for this before first deployment.
Setup NoteWaveshare Wiki / Developer community
Impressive performance for the price. Quiet operation even under load. JetPack is intuitive and packed with features. A serious edge AI platform that does not compromise on the software stack.
PositiveJeremy Morgan, developer review, Dec 2024
How It Compares
Deployment Kit vs Raspberry Pi 5 with Coral Accelerator
The Raspberry Pi 5 paired with a Google Coral USB Accelerator is a lower-cost option for constrained AI inference tasks. It lacks CUDA, TensorRT, and the broader NVIDIA software stack. For deployments where the AI model is fixed and well-supported by Coral's TPU, this combination is lower-cost. For any application requiring CUDA-accelerated inference, model flexibility, or future model updates, the Orin Nano Deployment Kit is the correct choice.
Verdict
The Most Practical Edge AI Deployment System Think Robotics Offers
The NVIDIA Jetson Orin Super Nano Deployment Kit, assembled in India, is the most practical edge AI system Think Robotics offers for teams moving from development to deployment. The combination of NVIDIA Orin Nano compute, the Waveshare production carrier board, preinstalled NVMe and wireless, an Indian three-pin power supply, and a local three-year module warranty makes this a complete solution with no hidden sourcing requirements.
For individuals and small teams still in the prototyping stage, the standard Orin Nano Super Developer Kit at $249 remains the right starting point. For teams shipping units to field locations, this kit removes the configuration overhead and sourcing steps that slow down deployment. Bulk pricing is available for enterprise and institutional buyers through Think Robotics directly.
Assembled in IndiaNVMe + Wireless preinstalledIndian 3-pin power adapter3-year module warranty67 TOPS (8GB Super Mode)Bulk pricing available
Frequently Asked Questions
The kit ships with JetPack preloaded on the NVMe SSD, so you can boot and begin development immediately after connecting power and a display. To access Super Mode performance on the 8GB variant (67 TOPS), you will need to upgrade to JetPack 6.2 using a host Ubuntu machine. The upgrade process is documented in Waveshare's wiki and typically takes 30 to 45 minutes. Note that this upgrade cannot be done through NVIDIA SDK Manager for this carrier board variant; a flashing script must be used instead.
The Deployment Kit is assembled in India, ships with an Indian three-pin power adapter, comes with NVMe storage and wireless already configured, and carries a three-year warranty on the NVIDIA module sourced through an authorized Indian distributor. Imported Developer Kits require separate storage, separate wireless hardware, and an international plug adapter. The Deployment Kit removes all of those steps in a single purchase from a locally accountable source.
Yes. The Waveshare JETSON-ORIN-IO-BASE carrier board accepts a wide input voltage range of 9V to 19V DC, which is compatible with standard robot battery packs and industrial power supplies. The Orin Nano module itself draws 7W to 25W depending on the active power mode, making it suitable for battery-powered mobile systems with reasonable runtime.
For single-camera object detection using YOLOv8 or similar models, the 4GB variant is sufficient. For multi-camera setups, models running simultaneously, or any use case involving transformer-based models or generative AI, the 8GB variant is the correct choice. The 8GB also gains Super Mode in JetPack 6.2, raising its ceiling from 40 TOPS to 67 TOPS without any hardware change.
Think Robotics assembles this kit in India and offers custom bulk pricing for enterprise customers, research institutions, and industrial buyers deploying multiple units. The team provides pre-sales technical guidance, helps with configuration advice for specific applications, and can coordinate volume shipments. Contact Think Robotics directly for bulk pricing and institutional purchase support on the Deployment Kit product page.
Ready to Deploy? Get the Kit That Ships Complete.
Assembled in India. NVMe + wireless preinstalled. Indian power adapter. 3-year module warranty. Bulk pricing available for enterprise and institutional buyers.
Please note that although my status in your website shows fulfilled, my components haven't arrived yet. I am extremely disappointed by the poor coordination between your team and the delivery team.
