NVIDIA Jetson advanced AI system: build autonomous machines for manufacturing, retail, robotics, smart farming, and healthcare
Buy the latest NVIDIA Jetson Production module powered edge vices, carrier boards, and add-ons at Seeed
About NVIDIA Jetson Modules
NVIDIA® Jetson™ brings accelerated AI performance to the Edge in a power-efficient and compact form factor. The Jetson family of modules all use the same NVIDIA CUDA-X™ software, and support cloud-native technologies like containerization and orchestration to build, deploy, and manage AI at the edge. Together with NVIDIA JetPack™ SDK, Jetson modules will help you develop and deploy innovative products across all industries.
Jetson Module and devices Lineup
Jetson AGX Orin Series
| JETSON AGX ORIN 32GB | JETSON AGX ORIN 64GB | ||
| AI Performance | 200 TOPS (INT8) | 275 TOPS (INT8) | |
| GPU | NVIDIA Ampere architecture with 1792 NVIDIA® CUDA® cores and 56 Tensor Cores | NVIDIA Ampere architecture with 2048 NVIDIA® CUDA® cores and 64 Tensor Cores | |
| Max GPU Freq | 939 MHz | 1.3 GHz | |
| CPU | 8-core Arm® Cortex®-A78AEv8.2 64-bit CPU2MB L2 + 4MB L3 | 12-core Arm® Cortex®-A78AEv8.2 64-bit CPU3MB L2 + 6MB L3 | |
| CPU Max Freq | 2.2 GHz | ||
| DL Accelerator | 2x NVDLA v2.0 | ||
| DLA Max Freq | 1.4 GHz | 1.6 GHz | |
| Vision Accelerator | PVA v2.0 | ||
| Memory | 32GB 256-bit LPDDR5204.8 GB/s | 64GB 256-bit LPDDR5204.8 GB/s | |
| Storage | 64GB eMMC 5.1 | ||
| CSI Camera | Up to 6 cameras (16 via virtual channels*)16 lanes MIPI CSI-2D-PHY 2.1 (up to 40Gbps) | C-PHY 2.0 (up to 164Gbps) | ||
| Video Encode | 1x 4K60 | 3x 4K30 | 6x 1080p60 |12x 1080p30 (H.265)H.264, AV1 | 2x 4K60 | 4x 4K30 | 8x 1080p60 |16x 1080p30 (H.265)H.264, AV1 | |
| Video Decode | 1x 8K30 | 2x 4K60 | 4x 4K30 | 9x 1080p60|18x 1080p30 (H.265)H.264, VP9, AV1 | 1x 8K30 | 3x 4K60 | 7x 4K30 | 11x 1080p60|22x 1080p30 (H.265)H.264, VP9, AV1 | |
| UPHY | Up to 2 x8, 1 x4, 2 x1 (PCIe Gen4, Root Port & Endpoint)3x USB 3.2Single lane UFS | ||
| Networking | 1x GbE4x 10GbE | ||
| Display | 1x 8K60 multi-mode DP 1.4a (+MST)/eDP 1.4a/HDMI 2.1 | ||
| Other I/O | 4x USB 2.04x UART, 3x SPI, 4x I2S, 8x I2C, 2x CAN, DMIC & DSPK, GPIOs | ||
| Power | 15W – 40W | 15W – 60W | |
| Mechanical | 100mm x 87mm699-pin Molex Mirror Mezz ConnectorIntegrated Thermal Transfer Plate |
Jetson AGX Orin Dev Kit
Bring your next-gen products to life with the world’s most powerful AI computer for energy-efficient autonomous machines. Up to 275 TOPS and 8X the performance of the last generation for multiple concurrent AI inference pipelines and high-speed interface support for multiple sensors make this the ideal solution for applications from manufacturing and logistics to retail and healthcare.
reServer with Jetson AGX Orin 64GB
reServer Jetson is a powerful inference server on the edge powered by Jetson AGX ORIN 64GB, which delivers up to 275 TOPs AI performance. It provides a high-speed 10-Gigabit Ethernet port and supports hybrid connectivity including 5G, LoRa, BLE and WiFi. reServer Jetson with pre-installed Triton Inference server is ready for fast and scalable AI in production and concurrent model execution.
Also check out our blog Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage
Jetson Orin NX Series
Jetson Orin NX modules deliver up to 100 TOPS of AI performance in the smallest Jetson form factor, with power configurable between 10W and 25W. The Orin NX Series is form factor compatible with the Jetson Xavier NX series and delivers up to 5x the performance, or up to 3X the performance at the same price.
| Jetson Xavier NX Series | Jetson Orin NX Series | |||
| Jetson Xavier NX 16GB | Jetson Xavier NX | Jetson Orin NX 8GB | Jetson Orin NX 16GB | |
| $499 | $399 | $399 | $599 | |
| AI Performance | 21 TOPs | 21 TOPs | 70 TOPS | 100 TOPS |
| GPU | 384-core NVIDIA Volta™ GPU with 48 Tensor Cores | 384-core NVIDIA Volta™ GPU with 48 Tensor Cores | 1024-core NVIDIA Ampere GPU with 32 Tensor Cores | 1024-core NVIDIA Ampere GPU with 32 Tensor Cores |
| CPU | 6-core NVIDIA Carmel Arm®v8.2 64-bit CPU 6MB L2 + 4MB L3 | 6-core NVIDIA Carmel Arm®v8.2 64-bit CPU 6MB L2 + 4MB L3 | 6-core NVIDIA Arm® Cortex A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 | 8-core NVIDIA Arm® Cortex A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3 |
| DL Accelerator | 2x NVDLA v1 | 2x NVDLA v1 | 1x NVDLA v2.0 | 2x NVDLA v2.0 |
| Memory | 16 GB | 8 GB | 8 GB | 16 GB |
| Storage | 16 GB eMMC 5.1 * | 16 GB eMMC 5.1 * | — (Supports external NVMe) | — (Supports external NVMe) |
| Power | 10W | 15W | 20W | 10W | 15W | 20W | 10W – 20W | 10W – 25W |
| Mechanical | 69.6 mm x 45 mm260-pin SO-DIMM connector | 69.6 mm x 45 mm 260-pin SO-DIMM connector | 69.6mm x 45mm 260-pin SO-DIMM connector | 69.6mm x 45mm 260-pin SO-DIMM connector |
Jetson AGX Xavier
| AI Performance | 32 TOPs |
| GPU | 512-core Volta GPU with 64 Tensor Cores11 TFLOPS (FP16)22 TOPS (INT8) |
| CPU | 8-core Carmel ARM v8.2 64-bit CPU, 8MB L2 + 4MB L3 |
| Memory | 32GB 256-Bit LPDDR4x | 136.5GB/s |
| Storage | 32GB eMMC 5.1 |
| DL Accelerator | (2x) NVDLA Engines* | 5 TFLOPS (FP16), 10 TOPS (INT8) |
| Vision Accelerator | 7-way VLIW Vision Processor* |
| Video Encode | 2x1000MP/sec4x 4K @ 60 (HEVC)8x 4K @ 30 (HEVC)16x 1080p @ 60 (HEVC)32x 1080p @ 30 (HEVC) |
| Video Decode | 2x1500MP/sec2x 8K @ 30 (HEVC)6x 4K @ 60 (HEVC)12x 4K @ 30 (HEVC)26x 1080p @ 60 (HEVC)52x 1080p @ 30 (HEVC)30x 1080p @ 30 (H.264) |
| Size | 105 mm x 105 mm |
Jetson Xavier AGX H01 Kit
The Jetson Xavier AGX H01 Development Kit is powered by NVIDIA Jetson AGX Xavier processor which applies AI performance and delivers up to 32 Tera Operations Per Second(TOPs) yet costs less than 30W. It is an ideal solution to agriculture, optical inspection, manufacturing, robotics, logistics, retail, service, smart cities, and healthcare.
Jetson Xavier NX
Xavier NX can achieve 172 FPS for PeopleNet- ResNet34 of People Detection, 274 FPS for DashCamNet-ResNet18 of Vehicle Detection, and 1126 FPS for FaceDetect-IR-ResNet18 of Face Detection. Benchmark details can be found on NVIDIA®’s DeepStream SDK website.
| AI Performance | 21 TOPS (INT8) |
| GPU | 384-core NVIDIA Volta™ GPU with 48 Tensor Cores |
| GPU Max Freq | 1100 MHz |
| CPU | 6-core NVIDIA Carmel ARM®v8.2 64-bit CPU6MB L2 + 4MB L3 |
| CPU Max Freq | 2-core @ 1900MHz4/6-core @ 1400Mhz |
| Memory | 8 GB 128-bit LPDDR4x @ 1866MHz, 59.7GB/s 16 GB 128-bit LPDDR4x @ 1866 MHz, 59.7GB/s |
| Storage | 16 GB eMMC 5.1 |
| Power | 10W|15W|20W |
| PCIe | 1 x1 + 1×4(PCIe Gen3, Root Port & Endpoint) |
| CSI Camera | Up to 6 cameras (36 via virtual channels)12 lanes MIPI CSI-2D-PHY 1.2 (up to 30 Gbps) |
| Video Encode | 2x 4K60 | 4x 4K30 | 10x 1080p60 | 22x 1080p30 (H.265)2x 4K60 | 4x 4K30 | 10x 1080p60 | 20x 108p30 (H.264) |
| Video Decode | 2x 8K30 | 6x 4K60 | 12x 4K30 | 22x 1080p60 | 44x 1080p30 (H.265)2x 4K60 | 6x 4K30 | 10x 1080p60 | 22x 1080p30 (H.264) |
| Display | 2 multi-mode DP 1.4/eDP 1.4/HDMI 2.0 |
| DL Accelerator | 2x NVDLA Engines |
| Vision Accelerator | 7-Way VLIW Vision Processor |
| Networking | 10/100/1000 BASE-T Ethernet |
| Mechanical | 45 mm x 69.6 mm260-pin SO-DIMM connector |
Jetson Nano
| GPU | NVIDIA Maxwell architecture with 128 NVIDIA CUDA® cores |
| CPU | Quad-core ARM Cortex-A57 MPCore processor |
| Memory | 4 GB 64-bit LPDDR4, 1600MHz 25.6 GB/s |
| Storage | 16 GB eMMC 5.1 |
| Video Encode | 250MP/sec1x 4K @ 30 (HEVC)2x 1080p @ 60 (HEVC)4x 1080p @ 30 (HEVC)4x 720p @ 60 (HEVC)9x 720p @ 30 (HEVC) |
| Video Decode | 500MP/sec1x 4K @ 60 (HEVC)2x 4K @ 30 (HEVC)4x 1080p @ 60 (HEVC)8x 1080p @ 30 (HEVC)9x 720p @ 60 (HEVC) |
| Camera | 12 lanes (3×4 or 4×2) MIPI CSI-2 D-PHY 1.1 (1.5 Gb/s per pair) |
| Connectivity | Gigabit Ethernet, M.2 Key E |
| Display | HDMI 2.0 and eDP 1.4 |
| USB | 4x USB 3.0, USB 2.0 Micro-B |
| Others | GPIO, I2C, I2S, SPI, UART |
| Mechanical | 69.6 mm x 45 mm260-pin edge connector |
reComputer Nano/NX: real world AI at the Edge, starts from $199
Built with Jetson Nano 4GB/ Xavier NX 8GB/16GB
- Edge AI box fit into anywhere
- Embedded Jetson Nano/NX Module
- Pre-installed Jetpack for easy deployment
- Nearly same form factor with Jetson Developer Kits, with rich set of I/Os
- Stackable and expandable
Jetson Benchmark: Jetson Xavier NX and Jetson AGX Orin MLPerf v2.0 Results
| Model | Jetson Xavier NX | Jetson AGX Xavier | Jetson AGX Orin |
| PeopleNet | 124 | 196 | 536 |
| Action Recognition 2D | 245 | 471 | 1577 |
| Action Recognition 3D | 21 | 32 | 105 |
| LPR Net | 706 | 1190 | 4118 |
| Dashcam Net | 425 | 671 | 1908 |
| Bodypose Net | 105 | 172 | 559 |
| ASR: Citrinet 1024 | 27 | 34 | 113 |
| NLP: BERT-base | 58 | 94 | 287 |
| TTS: Fastpitch-HifiGAN | 7 | 9 | 42 |
Get started with Jetson Software with necessary ML tools
Jetson Platform includes all the necessary ML tools to accelerate your next AI product quickly to market. At the base of the software stack, JetPack SDK includes the Board Support Package (BSP), with boot loader, Linux kernel, drivers, toolchains, and a reference file system based on Ubuntu.
Jetson Software comprises JetPack SDK, TAO & Pretrained AI Models from the NVIDIA NGC™ catalog, Triton Inference Server, NVIDIA Riva, DeepStream SDK, and NVIDIA Isaac.
Jetson Software Ecosystem 📷: NVIDIA
Develop stream analytics with the NVIDIA DeepStream.
NVIDIA’s DeepStream SDK delivers a complete streaming analytics toolkit for AI-based multi-sensor processing, video, audio and image understanding.
DeepStream offers exceptional throughput for a wide variety of object detection, image classification and instance segmentation based AI models. To reduce development efforts and increase throughput, developers can use highly accurate pre-trained models from TAO Toolkit and deploy with DeepStream. The following table shows the end-to-end application performance from data ingestion, decoding, image processing to inference. It takes multiple 1080p/30fps streams as input. Note that running on the DLAs for Jetson Xavier NX and Jetson AGX Xavier frees up GPU for other tasks.
| Jetson Nano* | Jetson TX2* | Jetson Xavier NX | Jetson AGX Xavier | |||||||||
| Application | Models | Inference Resolution | Precision | Model Accuracy | GPU (FPS*) | GPU (FPS) | GPU (FPS) | DLA1 (FPS) | DLA2 (FPS) | GPU (FPS) | DLA1 (FPS) | DLA2 (FPS) |
| People Detect | PeopleNet-ResNet34 | 960×544 | INT8 | 84% | 12 | 31 | 172 | 48 | 48 | 305 | 53 | 53 |
| Vehicle Detect | TrafficCamNet-ResNet18 | 960×544 | INT8 | 84% | 19 | 51 | 274 | 89 | 89 | 486 | 111 | 111 |
| Vehicle Detect | DashCamNet-ResNet18 | 960×544 | INT8 | 80% | 18 | 46 | 261 | 91 | 91 | 460 | 116 | 116 |
| Face Detect | FaceDetect-IR-ResNet18 | 384×240 | INT8 | 96% | 101 | 276 | 1126 | 455 | 455 | 2007 | 624 | 624 |
Tabulated data is in FPS with 1080p/30fps input. Batch size is equivalent to the number of input streams.which is total FPS from the table divide by 30.
* FP16 inference on Jetson Nano and Tx2
Jetson AI courses and certifications
NVIDIA’s Deep Learning Institute (DLI) delivers practical, hands-on training and certification in AI at the edge for developers, educators, students, and lifelong learners. This is a great way to get the critical AI skills you need to thrive and advance in your career. You can even earn certificates to demonstrate your understanding of Jetson and AI when you complete these free, open-source courses.
Seeed get started guide for Jetson platform
SSD boot instruction through SDK manager or command lines
Memory Expansion for reComputer Nano
The reComputer Jetson is sold with 16 GB of eMMC and has ubuntu 18.04 LTS and NVIDIA JetPack 4.6 installed, so the remaining user space available is about 2 GB, which is a significant obstacle to using the reComputer Jetson for training and deployment in some projects. This tutorial will introduce the expansion process for different models of reComputer Jetson based on this situation and help developers to expand their systems by transferring them to external storage devices.
