NVIDIA® Jetson™ TX2 series modules give you exceptional speed and power efficiency in an embedded AI computing device. Each supercomputer-on-a-module brings true AI computing to the edge with an NVIDIA Pascal™ GPU, up to 8 GB of memory and 59.7 GB/s of memory bandwidth, and a wide range of standard hardware interfaces.
NVIDIA Jetson TX2 NX delivers the next step in AI performance for entry-level embedded and edge products. It’s compact, power-efficient, and ideal for your next AI solution, from manufacturing and retail to agriculture and life sciences. Pre-trained AI models, Transfer Learning Toolkit, and the NVIDIA JetPack™ SDK help you get your best product to market—fast.
Features
SIze
MINIMIZE YOUR FOOTPRINT Now you can get exceptionally high compute, accuracy, and power efficiency in a module the size of a credit card. Its small 50 mm x 87 mm size enables real deep learning applications in small form-factor products like drones and more.
Performance
MAXIMIZE YOUR PERFORMANCE Experience more than double the performance or twice the energy efficiency of Jetson TX1. It’s all made possible by Jetson TX2’s 256-core NVIDIA Pascal architecture and 8 GB memory for the fastest compute and inference.
Power
OPTIMIZE YOUR POWER EFFICIENCY With Jetson TX2, you can now run large, deep neural networks for higher accuracy on edge devices. At just 7.5 watts, it delivers 25X more energy efficiency than a state-of-the-art desktop-class CPU. This makes it ideal for real-time processing in applications where bandwidth and latency can be an issue. These include factory robots, commercial drones, enterprise collaboration devices, intelligent cameras for smart cities.
A JETSON TX2 FOR ANY APPLICATION The extended Jetson TX2 family of embedded modules provides up to 2.5X the performance of Jetson Nano in as little as 7.5 W. Jetson TX2 NX offers pin and form-factor compatibility with Jetson Nano, while Jetson TX2, TX2 4GB, and TX2i all share the original Jetson TX2 form-factor. The rugged Jetson TX2i is ideal for settings including industrial robots and medical equipment.
Specifications
AI Performance
1.33 TFLOPS
GPU
NVIDIA Pascal™architecture with 256 NVIDIA®CUDA®cores
CPU
Dual-core NVIDIA Denver 2 64-bit CPU and quad-core Arm®Cortex®-A57 MPCore processor complex
Memory
4 GB 128-bit LPDDR4 51.2 GB/s
Storage
16 GB eMMC 5.1
Power
7.5W | 15W
PCIe
1 x1 + 1 x2 PCIe Gen2, total 30 GT/s
CSI Camera
Up to 5 cameras (12 via virtual channels) 12 lanes MIPI CSI-2 (3x4 or 5x2) D-PHY 1.2 (up to 30 Gbps)
{"id":6620744646742,"title":"NVIDIA Jetson TX2 NX","handle":"nvidia-jetson-tx2-nx-online","description":"\u003cp\u003eNVIDIA® Jetson™ TX2 series modules give you exceptional speed and power efficiency in an embedded AI computing device. Each supercomputer-on-a-module brings true AI computing to the edge with an NVIDIA Pascal™ GPU, up to 8 GB of memory and 59.7 GB\/s of memory bandwidth, and a wide range of standard hardware interfaces.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eNVIDIA Jetson TX2 NX delivers the next step in AI performance for entry-level embedded and edge products. It’s compact, power-efficient, and ideal for your next AI solution, from manufacturing and retail to agriculture and life sciences. Pre-trained AI models, Transfer Learning Toolkit, and the NVIDIA JetPack\u003c\/span\u003e\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e SDK help you get your best product to market—fast.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch5\u003eFeatures\u003c\/h5\u003e\n\u003cp\u003eSIze\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMINIMIZE YOUR FOOTPRINT\u003c\/strong\u003e\u003cbr\u003eNow you can get exceptionally high compute, accuracy, and power efficiency in a module the size of a credit card. Its small 50 mm x 87 mm size enables real deep learning applications in small form-factor products like drones and more.\u003c\/p\u003e\n\u003cp\u003ePerformance \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAXIMIZE YOUR PERFORMANCE\u003c\/strong\u003e\u003cbr\u003eExperience more than double the performance or twice the energy efficiency of Jetson TX1. It’s all made possible by Jetson TX2’s 256-core NVIDIA Pascal architecture and 8 GB memory for the fastest compute and inference.\u003c\/p\u003e\n\u003cp\u003ePower\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIMIZE YOUR POWER EFFICIENCY\u003c\/strong\u003e\u003cbr\u003eWith Jetson TX2, you can now run large, deep neural networks for higher accuracy on edge devices. At just 7.5 watts, it delivers 25X more energy efficiency than a state-of-the-art desktop-class CPU. This makes it ideal for real-time processing in applications where bandwidth and latency can be an issue. These include factory robots, commercial drones, enterprise collaboration devices, intelligent cameras for smart cities.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eA JETSON TX2 FOR ANY APPLICATION\u003c\/strong\u003e\u003cbr\u003eThe extended Jetson TX2 family of embedded modules provides up to 2.5X the performance of Jetson Nano in as little as 7.5 W. Jetson TX2 NX offers pin and form-factor compatibility with Jetson Nano, while Jetson TX2, TX2 4GB, and TX2i all share the original Jetson TX2 form-factor. The rugged Jetson TX2i is ideal for settings including industrial robots and medical equipment.\u003c\/p\u003e\n\u003ch5\u003eSpecifications\u003c\/h5\u003e\n\u003ctable style=\"width: 474.625px;\" cellspacing=\"0\" cellpadding=\"0\" border=\"1\" align=\"center\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eAI Performance\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1.33 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eGPU\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eNVIDIA Pascal\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003earchitecture with 256 NVIDIA\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003eCUDA\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003ecores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eCPU\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eDual-core NVIDIA Denver 2 64-bit CPU and quad-core Arm\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003eCortex\u003csup\u003e®\u003c\/sup\u003e-A57 MPCore processor complex\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eMemory\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e4 GB 128-bit LPDDR4\u003cbr\u003e51.2 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e16 GB eMMC 5.1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003ePower\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e7.5W | 15W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003ePCIe\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1 x1 + 1 x2\u003cbr\u003ePCIe Gen2, total 30 GT\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eCSI Camera\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eUp to 5 cameras (12 via virtual channels)\u003cbr\u003e12 lanes MIPI CSI-2 (3x4 or 5x2)\u003cbr\u003eD-PHY 1.2 (up to 30 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eVideo Encode\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1x 4K60 | 3x 4K30 | 4x 1080p60 | 8x 1080p30 (H.265)\u003cbr\u003e1x 4K60 | 3x 4K30 | 7x 1080p60 | 14x 1080p30 (H.264)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eVideo Decode\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e2x 4K60 | 4x 4K30 | 7x 1080p60 | 14x 1080p30 (H.265 \u0026amp; H.264)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eDisplay\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e2 multi-mode DP 1.2\/eDP 1.4\/HDMI 2.0\u003cbr\u003e1x 2 DSI (1.5Gbps\/lane)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eNetworking\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e10\/100\/1000 BASE-T Ethernet\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eMechanical\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px; text-align: left;\" class=\"tableCRdata\" colspan=\"2\"\u003e69.6 mm x 45 mm\u003cbr\u003e260-pin SO-DIMM edge connector\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","published_at":"2022-12-06T11:11:50+05:30","created_at":"2021-08-24T15:34:25+05:30","vendor":"ThinkRobotics","type":"Single Board Computers","tags":["Jetson GPU","Jetson module","jetson nano","Jetson Nano Case","jetson xavier","JT-SOM","NVDA","nvidia","nvidia gpu","nvidia jetson","nvidia nx","nvidia tx2","NVIDIA-COM","SBC1","tx2","tx2 nx","tx2nx"],"price":2049999,"price_min":2049999,"price_max":2049999,"available":true,"price_varies":false,"compare_at_price":2799999,"compare_at_price_min":2799999,"compare_at_price_max":2799999,"compare_at_price_varies":false,"variants":[{"id":39516436791382,"title":"Default Title","option1":"Default Title","option2":null,"option3":null,"sku":"SBC1103-NX","requires_shipping":true,"taxable":true,"featured_image":null,"available":true,"name":"NVIDIA Jetson TX2 NX","public_title":null,"options":["Default Title"],"price":2049999,"weight":799,"compare_at_price":2799999,"inventory_management":"shopify","barcode":"39516436791382","requires_selling_plan":false,"selling_plan_allocations":[]}],"images":["\/\/thinkrobotics.com\/cdn\/shop\/products\/Image1_88553d47-d730-4de0-aad7-4a8a9dfe6b57.png?v=1629799470","\/\/thinkrobotics.com\/cdn\/shop\/products\/jetson_tx2_nx_3qtr_front-right.png?v=1629799470"],"featured_image":"\/\/thinkrobotics.com\/cdn\/shop\/products\/Image1_88553d47-d730-4de0-aad7-4a8a9dfe6b57.png?v=1629799470","options":["Title"],"media":[{"alt":"NVIDIA Jetson TX2 NX Online","id":21213032874070,"position":1,"preview_image":{"aspect_ratio":1.0,"height":1000,"width":1000,"src":"\/\/thinkrobotics.com\/cdn\/shop\/products\/Image1_88553d47-d730-4de0-aad7-4a8a9dfe6b57.png?v=1629799470"},"aspect_ratio":1.0,"height":1000,"media_type":"image","src":"\/\/thinkrobotics.com\/cdn\/shop\/products\/Image1_88553d47-d730-4de0-aad7-4a8a9dfe6b57.png?v=1629799470","width":1000},{"alt":"NVIDIA Jetson TX2 NX Online","id":21213032906838,"position":2,"preview_image":{"aspect_ratio":1.004,"height":1840,"width":1848,"src":"\/\/thinkrobotics.com\/cdn\/shop\/products\/jetson_tx2_nx_3qtr_front-right.png?v=1629799470"},"aspect_ratio":1.004,"height":1840,"media_type":"image","src":"\/\/thinkrobotics.com\/cdn\/shop\/products\/jetson_tx2_nx_3qtr_front-right.png?v=1629799470","width":1848}],"requires_selling_plan":false,"selling_plan_groups":[],"content":"\u003cp\u003eNVIDIA® Jetson™ TX2 series modules give you exceptional speed and power efficiency in an embedded AI computing device. Each supercomputer-on-a-module brings true AI computing to the edge with an NVIDIA Pascal™ GPU, up to 8 GB of memory and 59.7 GB\/s of memory bandwidth, and a wide range of standard hardware interfaces.\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eNVIDIA Jetson TX2 NX delivers the next step in AI performance for entry-level embedded and edge products. It’s compact, power-efficient, and ideal for your next AI solution, from manufacturing and retail to agriculture and life sciences. Pre-trained AI models, Transfer Learning Toolkit, and the NVIDIA JetPack\u003c\/span\u003e\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e SDK help you get your best product to market—fast.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch5\u003eFeatures\u003c\/h5\u003e\n\u003cp\u003eSIze\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMINIMIZE YOUR FOOTPRINT\u003c\/strong\u003e\u003cbr\u003eNow you can get exceptionally high compute, accuracy, and power efficiency in a module the size of a credit card. Its small 50 mm x 87 mm size enables real deep learning applications in small form-factor products like drones and more.\u003c\/p\u003e\n\u003cp\u003ePerformance \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAXIMIZE YOUR PERFORMANCE\u003c\/strong\u003e\u003cbr\u003eExperience more than double the performance or twice the energy efficiency of Jetson TX1. It’s all made possible by Jetson TX2’s 256-core NVIDIA Pascal architecture and 8 GB memory for the fastest compute and inference.\u003c\/p\u003e\n\u003cp\u003ePower\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPTIMIZE YOUR POWER EFFICIENCY\u003c\/strong\u003e\u003cbr\u003eWith Jetson TX2, you can now run large, deep neural networks for higher accuracy on edge devices. At just 7.5 watts, it delivers 25X more energy efficiency than a state-of-the-art desktop-class CPU. This makes it ideal for real-time processing in applications where bandwidth and latency can be an issue. These include factory robots, commercial drones, enterprise collaboration devices, intelligent cameras for smart cities.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eA JETSON TX2 FOR ANY APPLICATION\u003c\/strong\u003e\u003cbr\u003eThe extended Jetson TX2 family of embedded modules provides up to 2.5X the performance of Jetson Nano in as little as 7.5 W. Jetson TX2 NX offers pin and form-factor compatibility with Jetson Nano, while Jetson TX2, TX2 4GB, and TX2i all share the original Jetson TX2 form-factor. The rugged Jetson TX2i is ideal for settings including industrial robots and medical equipment.\u003c\/p\u003e\n\u003ch5\u003eSpecifications\u003c\/h5\u003e\n\u003ctable style=\"width: 474.625px;\" cellspacing=\"0\" cellpadding=\"0\" border=\"1\" align=\"center\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eAI Performance\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1.33 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eGPU\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eNVIDIA Pascal\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003earchitecture with 256 NVIDIA\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003eCUDA\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003ecores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eCPU\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eDual-core NVIDIA Denver 2 64-bit CPU and quad-core Arm\u003csup\u003e®\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003eCortex\u003csup\u003e®\u003c\/sup\u003e-A57 MPCore processor complex\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eMemory\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e4 GB 128-bit LPDDR4\u003cbr\u003e51.2 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e16 GB eMMC 5.1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003ePower\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e7.5W | 15W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003ePCIe\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1 x1 + 1 x2\u003cbr\u003ePCIe Gen2, total 30 GT\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eCSI Camera\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003eUp to 5 cameras (12 via virtual channels)\u003cbr\u003e12 lanes MIPI CSI-2 (3x4 or 5x2)\u003cbr\u003eD-PHY 1.2 (up to 30 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eVideo Encode\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e1x 4K60 | 3x 4K30 | 4x 1080p60 | 8x 1080p30 (H.265)\u003cbr\u003e1x 4K60 | 3x 4K30 | 7x 1080p60 | 14x 1080p30 (H.264)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eVideo Decode\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e2x 4K60 | 4x 4K30 | 7x 1080p60 | 14x 1080p30 (H.265 \u0026amp; H.264)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eDisplay\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e2 multi-mode DP 1.2\/eDP 1.4\/HDMI 2.0\u003cbr\u003e1x 2 DSI (1.5Gbps\/lane)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eNetworking\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px;\" class=\"tableCRdata\" colspan=\"2\"\u003e10\/100\/1000 BASE-T Ethernet\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 89px;\" class=\"tableCLdata\"\u003eMechanical\u003c\/td\u003e\n\u003ctd style=\"width: 379.625px; text-align: left;\" class=\"tableCRdata\" colspan=\"2\"\u003e69.6 mm x 45 mm\u003cbr\u003e260-pin SO-DIMM edge connector\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e"}
