Nvidia Jetson

{{short description|Series of embedded computing boards by Nvidia}}

Nvidia Jetson is a series of embedded computing boards from Nvidia. The Jetson TK1, TX1 and TX2 models all carry a Tegra processor (or SoC) from Nvidia that integrates an ARM architecture central processing unit (CPU). Jetson is a low-power system and is designed for accelerating machine learning applications.

Hardware

File:Nvidia Jetson TK1 - Full Board (14672953894).png

The Jetson family includes the following boards:

In late April 2014, Nvidia shipped the Nvidia Jetson TK1 development board containing a Tegra K1 SoC in the T124 variant and running Ubuntu Linux.{{cite web | url=https://www.phoronix.com/scan.php?page=news_item&px=MTY3NjA | title=NVIDIA's Tegra TK1 Jetson Board Is Now Shipping | date=29 April 2014 | author=Michael Larabel | publisher=Phoronix}}
The Nvidia Jetson TX1 development board bears a Tegra X1 of model T210.{{cite web|url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/ |title=Embedded Systems Development Solutions from NVIDIA Jetson |publisher=NVIDIA |date=2015-03-18 |access-date=2016-07-10}}
The Nvidia Jetson TX2 board bears a Tegra X2 of microarchitecture GP10B[http://www.phoronix.com/scan.php?page=news_item&px=Tegra-X2-Nouveau-Support NVIDIA Rolls Out Tegra X2 GPU Support In Nouveau] by Michael Larabel at phoronix.com on March 29, 2017 (SoC type T186 or very similar). This board and the associated development platform was announced in March 2017 as a compact card design for low power scenarios, e.g. for the use in smaller camera drones such as the Skydio 2.{{cite web |last1=Alarcon |first1=Nefi |title=Inception Spotlight: New Skydio 2 Drone Powered by NVIDIA Jetson |url=https://developer.nvidia.com/blog/skydio-2-jetson-tx2-drone/ |website=NVIDIA Technical Blog |publisher=NVIDIA Technical Blog |access-date=6 January 2025}} A matrix describing a set of performance modes was provided by the media along with that.[http://www.anandtech.com/show/11185/nvidia-announces-jetson-tx2-parker NVIDIA Announces Jetson TX2: Parker Comes To NVIDIA’s Embedded System Kit], March 7, 2017 Further a TX2i variant, said to be rugged and suitable for industrial use cases, is mentioned.{{Cite web|url=https://forums.developer.nvidia.com/t/nvidia-jetson-tx2i-module-for-industrial-environments/58962|title=NVIDIA Jetson TX2i Module for Industrial Environments|date=March 9, 2018|website=NVIDIA Developer Forums}}
The Nvidia Jetson Xavier was announced as a development kit in end of August 2018. Indications were given that a 20x acceleration for certain application cases compared to predecessor devices should be expected, and that the application power efficiency is 10x improved. Nvidia Jetson Xavier NX has a 6-core Nvidia Carmel ARMv8.2.
The Nvidia Jetson AGX Xavier is the 8-core version on the same core architecture (Carmel Armv8.2).{{Cite web|url=https://developer.nvidia.com/embedded/jetson-agx-xavier-developer-kit%20|title=Jetson AGX Xavier Developer Kit|date=July 9, 2018|website=NVIDIA Developer}}

File:NVIDIA Jetson Nano Developer Kit (47616885631) (cropped).jpg

The Nvidia Jetson Nano was announced as a development system in mid-March 2019{{Cite web|url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/|title=NVIDIA Jetson Nano For Edge AI Applications and Education|website=NVIDIA}} The intended market is for hobbyist robotics due to the low price point.{{Cite web|url=https://www.eteknix.com/nvidia-jetson-nano-99-of-cuda-x-awesomeness/|title=Nvidia Jetson Nano – $99 of CUDA X Awesomeness|date=March 18, 2019}}{{Cite web|url=https://hackaday.com/2019/03/18/hands-on-new-nvidia-jetson-nano-is-more-power-in-a-smaller-form-factor/|title=Hands-On: New Nvidia Jetson Nano Is More Power In A Smaller Form Factor|date=March 18, 2019}} The final specs expose the board being sort of a power-optimized, stripped-down version of what a full Tegra X1 system would mean. Only half of the CPU (only 4x A57 @ 1.43 GHz) and GPU (128 cores of Maxwell generation @ 921 MHz) cores are present and only half of the maximum possible RAM is attached (4 GB LPDDR4 @ 64 bit + 1.6 GHz = 25.6 GB/s) whilst the available or usable interfacing is determined by the baseboard design and is further subject of implementation decisions and specifics in an end user specific design for an application case.[https://www.cnx-software.com/2019/03/19/nvidia-jetson-nano-developer-kit/ NVIDIA Introduces $99 Jetson Nano Developer Kit] by Jean-Luc Aufranc on March 19, 2019 on CNXSoft
The Nvidia Jetson Nano Developer Kit.[https://forums.developer.nvidia.com/t/links-to-jetson-nano-resources-wiki/71639 Links to Jetson Nano Resources & Wiki][https://developer.download.nvidia.com/assets/embedded/secure/jetson/Nano/docs/NVIDIA_Jetson_Nano_Developer_Kit_User_Guide.pdf Nvidia Jetson Developer Kit User Guide]

File:NVidia Jetson Orin AGX.png

In September 2022 Nvidia announced the Jetson Orin Nano.{{Cite web |date=2022-09-21 |title=Solving Entry-Level Edge AI Challenges with NVIDIA Jetson Orin Nano |url=https://developer.nvidia.com/blog/solving-entry-level-edge-ai-challenges-with-nvidia-jetson-orin-nano/ |access-date=2022-10-27 |website=NVIDIA Technical Blog |language=en-US}}{{Cite web |last=Robinson |first=Cliff |date=2022-09-22 |title=NVIDIA Jetson Orin Nano Launched Cheaper Arm and Ampere |url=https://www.servethehome.com/nvidia-jetson-orin-nano-launched-cheaper-arm-and-ampere/ |access-date=2022-10-27 |website=ServeTheHome |language=en-US}} The modules have the same 260-pin SO-DIMM connector and 69.6 mm x 45 mm dimensions, and come in two variants. The 4 GB variant provides 20 Sparse or 10 Dense TOPs, using a 512-core Ampere GPU with 16 Tensor cores, while the 8 GB variant doubles those numbers to 40/20 TOPs, a 1024-core GPU and 16 Tensor cores. Both have 6 Arm Cortex-A78AE cores. The 4 GB module starts at $199 and the 8 GB variant for $299, when purchasing 1000 units.
In December 2024, Nvidia announced the updated Orin Nano with a claimed 1.7x performance improvement and a price reduction of the development kit from $499 to $249{{Cite web |date=2024-12-19 |title=NVIDIA Unveils Its Most Affordable Generative AI Supercomputer |url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit/ |access-date=2024-12-19}}

Performance

There are various versions of the Jetson board available.

class="wikitable"
Year ! Name ! Performance ! GPU ! DLA ! CPU ! Memory ! Power
2014 \| Jetson TK1 \| 0.327 Dense FP32 Total TFLOPS{{Cite web \|title=Low-Power Sensing and Autonomy With NVIDIA Jetson TK1 {{!}} Parallel Forall {{!}} NVIDIA Technical Blog \|author= \|work=NVIDIA Technical Blog \|date= \|access-date=23 February 2025 \|url= https://developer.nvidia.com/blog/low-power-sensing-autonomy-nvidia-jetson-tk1/}} \| 192-core Nvidia Kepler architecture GPU \| {{n/a}} \| Quad-core ARM Cortex-A15 processor \| 2 GiB \| 10 W
2015 \| Jetson TX1 \| 1.024 Dense FP16 TFLOPS{{Cite web \|title=NVIDIA Jetson TX1 Supercomputer-on-Module Drives Next Wave of Autonomous Machines {{!}} NVIDIA Technical Blog \|author= \|work=NVIDIA Technical Blog \|date= \|access-date=23 February 2025 \|url= https://developer.nvidia.com/blog/nvidia-jetson-tx1-supercomputer-on-module-drives-next-wave-of-autonomous-machines/}} \| 256-core Nvidia Maxwell architecture GPU \| {{n/a}} \| Quad-core ARM Cortex-A57 MPCore processor \| 4 GiB \| 10 W
2017 \| Jetson TX2{{cite web \| url=https://developer.nvidia.com/embedded/jetson-tx2 \| title=Jetson TX2 Module \| date=18 April 2023 \| author=NVIDIA \| publisher=Nvidia}} \| 1.333 Dense FP16 TFLOPS \| 256-core Nvidia Pascal architecture GPU \| {{n/a}} \| Dual-core Nvidia Denver 2 64-bit CPU and quad-core ARM Cortex-A57 MPCore processor \| 8 GiB \| 7.5–15 W
2018 \| Jetson AGX Xavier{{cite web \| url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/ \| title=Jetson AGX Xavier Series \| date=18 April 2023 \| author=NVIDIA \| publisher=Nvidia}} \| 30-32 Dense INT8 Total TOPS \| 512-core Nvidia Volta architecture GPU with 64 Tensor cores \| 2 \| 8-core NVIDIA Carmel ARMv8.2 64-bit CPU 8MB L2 + 4MB L3 \| 32–64 GiB \| 10-40 W
2019 \| Jetson Nano \| 0.472 Dense FP16 TFLOPS \| 128-core Nvidia Maxwell architecture GPU \| {{n/a}} \| Quad-core ARM Cortex-A57 MPCore processor \| 4 GiB \| 5–10 W
2020 \| Jetson Xavier NX \| 21 Dense INT8 Total TOPS \| 384-core Nvidia Volta architecture GPU with 48 Tensor cores \| 2 \| 6-core Nvidia Carmel ARMv8.2 64-bit CPU 6MB L2 + 4MB L3 \| 8 GiB \| 10–20 W
2023 \| Jetson Orin Nano{{cite web \| url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ \| title= Jetson Orin Modules and Developer Kits \| date=27 March 2023 \| author=NVIDIA \| publisher=Nvidia}} \| 34–67 Sparse INT8 TOPS \| up to 1024-core Nvidia Ampere architecture GPU with 32 Tensor cores \| {{n/a}} \| 6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3 \| 4–8 GiB \| 7–25 W
2023 \| Jetson Orin NX \| 117–157 Sparse INT8 Total TOPS \| 1024-core Nvidia Ampere architecture GPU with 32 Tensor cores \| 1-2 \| up to 8-core ARM Cortex-A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3 \| 8–16 GiB \| 10–40 W
2023 \| Jetson AGX Orin \| 200-275 Sparse INT8 Total TOPS \| up to 2048-core Nvidia Ampere architecture GPU with 64 Tensor cores \| 2 \| up to 12-core ARM Cortex-A78AE v8.2 64-bit CPU 3MB L2 + 6MB L3 \| 32–64 GiB \| 15–60 W

class="wikitable"

Year

! Name

! Performance

! GPU

! DLA

! CPU

! Memory

! Power

2014

| Jetson TK1

| 0.327 Dense FP32 Total TFLOPS{{Cite web |title=Low-Power Sensing and Autonomy With NVIDIA Jetson TK1 {{!}} Parallel Forall {{!}} NVIDIA Technical Blog |author= |work=NVIDIA Technical Blog |date= |access-date=23 February 2025 |url= https://developer.nvidia.com/blog/low-power-sensing-autonomy-nvidia-jetson-tk1/}}

| 192-core Nvidia Kepler architecture GPU

| {{n/a}}

| Quad-core ARM Cortex-A15 processor

| 2 GiB

| 10 W

2015

| Jetson TX1

| 1.024 Dense FP16 TFLOPS{{Cite web |title=NVIDIA Jetson TX1 Supercomputer-on-Module Drives Next Wave of Autonomous Machines {{!}} NVIDIA Technical Blog |author= |work=NVIDIA Technical Blog |date= |access-date=23 February 2025 |url= https://developer.nvidia.com/blog/nvidia-jetson-tx1-supercomputer-on-module-drives-next-wave-of-autonomous-machines/}}

| 256-core Nvidia Maxwell architecture GPU

| {{n/a}}

| Quad-core ARM Cortex-A57 MPCore processor

| 4 GiB

| 10 W

2017

| Jetson TX2{{cite web | url=https://developer.nvidia.com/embedded/jetson-tx2 | title=Jetson TX2 Module | date=18 April 2023 | author=NVIDIA | publisher=Nvidia}}

| 1.333 Dense FP16 TFLOPS

| 256-core Nvidia Pascal architecture GPU

| {{n/a}}

| Dual-core Nvidia Denver 2 64-bit CPU and quad-core ARM Cortex-A57 MPCore processor

| 8 GiB

| 7.5–15 W

2018

| Jetson AGX Xavier{{cite web | url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/ | title=Jetson AGX Xavier Series | date=18 April 2023 | author=NVIDIA | publisher=Nvidia}}

| 30-32 Dense INT8 Total TOPS

| 512-core Nvidia Volta architecture GPU with 64 Tensor cores

| 2

| 8-core NVIDIA Carmel ARMv8.2 64-bit CPU 8MB L2 + 4MB L3

| 32–64 GiB

| 10-40 W

2019

| Jetson Nano

| 0.472 Dense FP16 TFLOPS

| 128-core Nvidia Maxwell architecture GPU

| {{n/a}}

| Quad-core ARM Cortex-A57 MPCore processor

| 4 GiB

| 5–10 W

2020

| Jetson Xavier NX

| 21 Dense INT8 Total TOPS

| 384-core Nvidia Volta architecture GPU with 48 Tensor cores

| 2

| 6-core Nvidia Carmel ARMv8.2 64-bit CPU 6MB L2 + 4MB L3

| 8 GiB

| 10–20 W

2023

| Jetson Orin Nano{{cite web | url=https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ | title= Jetson Orin Modules and Developer Kits

| date=27 March 2023 | author=NVIDIA | publisher=Nvidia}}

| 34–67 Sparse INT8 TOPS

| up to 1024-core Nvidia Ampere architecture GPU with 32 Tensor cores

| {{n/a}}

| 6-core ARM Cortex-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3

| 4–8 GiB

| 7–25 W

2023

| Jetson Orin NX

| 117–157 Sparse INT8 Total TOPS

| 1024-core Nvidia Ampere architecture GPU with 32 Tensor cores

| 1-2

| up to 8-core ARM Cortex-A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3

| 8–16 GiB

| 10–40 W

2023

| Jetson AGX Orin

| 200-275 Sparse INT8 Total TOPS

| up to 2048-core Nvidia Ampere architecture GPU with 64 Tensor cores

| 2

| up to 12-core ARM Cortex-A78AE v8.2 64-bit CPU 3MB L2 + 6MB L3

| 32–64 GiB

| 15–60 W

= Maxwell architecture =

The published operation modes of the Nvidia Jetson Nano are:

class="wikitable"
Mode ! 0 ! 1
GPU clock (MHz) \| 921 \| 640
Cortex-A57 (MHz) \| 4x 1428 \| 2x 918 2x stopped
TDP (W) \| 10 \| 5

class="wikitable"

Mode

! 0

! 1

GPU clock (MHz)

| 921

| 640

Cortex-A57 (MHz)

| 4x 1428

| 2x 918
2x stopped

TDP (W)

| 10

| 5

= Pascal architecture =

class="wikitable" \|+ Jetson TX2 Series Comparison Table{{Cite web \|title=NVIDIA Jetson TX2: High Performance AI at the Edge \|author= \|work=NVIDIA \|date= \|access-date=24 February 2025 \|url= https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-tx2/}} ! Product Name !! GPU Performance Dense Tensor FP16 TFLOPS !! Memory GB !! Storage GB !! Power W
Jetson TX2 NX	1.33	4	16	7.5, 15
Jetson TX2 4GB	1.33	4	16	7.5, 15
Jetson TX2i	1.26	8	32	10, 20
Jetson TX2	1.33	8	32	7.5, 15

The published performance modes of the Nvidia Jetson TX2 are as follows.

class="wikitable"
Mode ! Max Clocks (Denver 2 + A57) ! Max-P (Denver 2 + A57) ! Max-P (only Denver 2) ! Max-P (only A57) ! Max-Q (only A57)
GPU clock (MHz) \| 1302 \| colspan = "3" \| 1122 \| 854
Denver 2 clock (MHz) \| 2000 \| 1400 \| 2000 \| stopped \| stopped
Cortex-A57 (MHz) \| 2000+ \| 1400 \| stopped \| 2000 \| 1200
TDP (W) \| might vary \| 15 \| 15 \| 15 \| 7.5

class="wikitable"

Mode

! Max Clocks
(Denver 2 + A57)

! Max-P
(Denver 2 + A57)

! Max-P
(only Denver 2)

! Max-P
(only A57)

! Max-Q
(only A57)

GPU clock (MHz)

| 1302

| colspan = "3" | 1122

| 854

Denver 2 clock (MHz)

| 2000

| 1400

| 2000

| stopped

Cortex-A57 (MHz)

| 2000+

| 1400

| stopped

| 2000

| 1200

TDP (W)

| might vary

| 15

| 7.5

Jetson TX2 also has 5 power modes, numbered 0 through 4 as published by NVIDIA.{{Cite web|title=Tegra Linux Driver (See under "NVPModel Clock Configuration for Jetson TX2 and TX2 4GB")|url=https://docs.nvidia.com/jetson/archives/l4t-archived/l4t-3231/index.html#page/Tegra%2520Linux%2520Driver%2520Package%2520Development%2520Guide/power_management_tx2_32.html#wwpID0E0VN0HA|access-date=2021-07-25|website=docs.nvidia.com}} The default mode is mode 3 (MAX-P).

class="wikitable"

|+Jetson TX2 clock configuration with power modes

!Property

!MAX-N
(Mode 0)

!MAX-Q
(Mode 1)

!MAX-P
(Mode 2)

!MAX-P*
(Mode 3)

!MAX-P
(Mode 4)

Power budget

|N/A

|7.5W

|15W

Online A57 CPU

Online D15 CPU

A57 CPU max freq (MHz)

|2000

|1200

|1400

|2000

|345

D15 CPU max freq (MHz)

|2000

|N/A

|1400

|N/A

|2000

GPU max freq (MHz)

|1300

|850

|1122

Memory max freq (MHz)

|1866

|1331

|1600

= Volta architecture =

Product Name	GPU Performance Dense Tensor INT8 TOPS	DLA Performance Dense Tensor INT8 TOPS	Memory GB	Storage GB	Power W
class="wikitable" \|+ Jetson Xavier Series Comparison Table{{Cite web \|title=NVIDIA Jetson Xavier Series \|author= \|work=NVIDIA \|date= \|access-date=23 February 2025 \|url= https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-xavier-series/}}
Jetson Xavier NX	13	8	8	16	10, 15, 20
Jetson Xavier NX 16GB	13	8	16	16	10, 15, 20
Jetson AGX Xavier	22	10	32	32	10, 15, 30
Jetson AGX Xavier Industrial	20	10	32	32	20, 40
Jetson AGX Xavier 64GB	22	10	64	64	10, 15, 30

= Ampere architecture =

Product Name	GPU Performance Dense Tensor INT8 TOPS	DLA Performance Dense Tensor INT8 TOPS	Memory GB	Storage GB	Power W
class="wikitable" \|+ Jetson Orin Series Comparison Table{{Cite web \|title=NVIDIA Jetson AGX Orin \|author= \|work=NVIDIA \|date= \|access-date=23 February 2025 \|url= https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/}}
Jetson Orin Nano 4GB	17	{{n/a}}	4	{{n/a}}	7, 10, 25
Jetson Orin Nano 8GB	33	{{n/a}}	8	{{n/a}}	7, 15, 25
Jetson Orin NX 8GB	38	20	8	{{n/a}}	10, 15, 25, 40
Jetson Orin NX 16GB	38	40	16	{{n/a}}	10, 15, 25, 40
Jetson AGX Orin 32GB	54	46	32	64	15, 40
Jetson AGX Orin Industrial	78	46	64	64	15, 75
Jetson AGX Orin 64GB	85	52.5	64	64	15, 60

Software

Various operating systems and software might be able to run on the Jetson board series.

=Linux=

JetPack is a software development kit (SDK) from Nvidia for their Jetson board series. It includes the Linux for Tegra (L4T) operating system and other tools. The official Nvidia download page bears an entry for JetPack 3.2 (uploaded there on 2018-03-08) that states:

{{blockquote|JetPack 3.2 adds support for the Linux for Tegra r28.2 image for the Jetson OS. It is packaged with newer versions of Tegra System Profiler, TensorRT, and cuDNN from the last release.{{Cite web|url=https://developer.nvidia.com/embedded/downloads|title=Jetson Download Center|date=November 3, 2015|website=NVIDIA Developer}}}}

RedHawk Linux is a high-performance RTOS available for the Jetson platform, along with associated NightStar real-time development tools, CUDA/GPU enhancements, and a framework for hardware-in-the-loop and man-in-the-loop simulations.{{Cite web|url=https://www.concurrent-rt.com/redhawk-linux-nvidia-jetson-support/|title=Concurrent products for the NVIDIA Jetson|website=Concurrent Real-Time Linux RTOS Solutions}}

=QNX=

The QNX operating system also available for the Jetson platform, though it is not widely announced. There are success reports of installing and running specific QNX packages on certain Nvidia Jetson board variants. Namely the package qnx-V3Q-23.16.01 that is seemingly in parts based on Nvidia's Vibrante Linux distribution is reported to run on the Jetson TK1 Pro board.{{Cite web|url=https://forums.developer.nvidia.com/t/running-qnx-onto-jetson-tk1-pro-1860/43158|title=Running QNX onto Jetson TK1 Pro (1860)|date=June 6, 2016|website=NVIDIA Developer Forums}}