AGX Xavier Developer Kit vs Jetson Nano
Comparison and Benchmark

Introduction

NVIDIA Jetson AGX Xavier is an embedded system-on-module (SoM) from the NVIDIA AGX Systems family. The Jetson AGX Xavier module makes AI-powered autonomous machines possible, running in as little as 10W and delivering up to 32 TOPs. It is a computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. It is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. 

NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 [USD] computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. NVIDIA® Jetson Nano Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing.

PlatformCPUGPUMemoryStorageMRP
Jetson AGX Xavier8x ARM v8.2 @ 2.26GHz512x Volta GPU @ 64 Tensor Cores32GB LPDDR4 (137GB/s)32GB eMMC$699
Jetson Nano4x ARM Cortex A57 @ 1.43 GHz128x Maxwell @ 921 MHz (472 GFLOPS)4GB LPDDR4 (25.6 GB/s)Micro SD$99

Jetson Nano vs AGX Xavier Specifications

FeaturesJetson Nano 4GBJetson Nano 2GBJetson AGX Xavier
CPUARM Cortex-A57 (quad-core) @ 1.43GHz ARM Cortex-A57 (quad-core) @ 1.43GHzNVIDIA Carmel ARMv8.2 (octal-core) @ 2.26GHz(4x2MB L2 + 4MB L3)
GPU128-core NVIDIA Maxwell @ 921MHz128-core NVIDIA Maxwell @ 921MHz 512-core Volta @ 1377 MHz + 64 Tensor Cores
DLNVIDIA GPU support (CUDA, cuDNN, TensorRT)NVIDIA GPU support (CUDA, cuDNN, TensorRT) Dual NVIDIA Deep Learning Accelerators
Memory4GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s2GB 64-bit LPDDR4 @ 1600MHz | 25.6 GB/s 32GB 256-bit LPDDR4x @ 2133MHz | 137GB/s
StorageMicroSD cardMicroSD card32GB eMMC 5.1
VisionNVIDIA GPU support (CUDA, VisionWorks, OpenCV)NVIDIA GPU support (CUDA, VisionWorks, OpenCV)7-way VLIW Vision Accelerator
Encoder(1x) 4Kp30, (2x) 1080p60, (4x) 1080p304Kp30 | 4x 1080p30 | 9x 720p30 (H.264/H.265)(4x) 4Kp60, (8x) 4Kp30, (32x) 1080p30
Decoder4Kp60, (2x) 4Kp30, (4x) 1080p60, (8x) 1080p304Kp60 | 2x 4Kp30 | 8x 1080p30 | 18x 720p30 (H.264/H.265)(2x) 8Kp30, (6x) 4Kp60, (12x) 4Kp30
Camera12 lanes MIPI CSI-2 | 1.5 Gbps per lane1x MIPI CSI-2 connector16 lanes MIPI CSI-2 | 6.8125Gbps per lane
Display2x HDMI 2.0 / DP 1.2 / eDP 1.2 | 2x MIPI DSIHDMI(3x) eDP 1.4 / DP 1.2 / HDMI 2.0 @ 4Kp60
WirelessM.2 Key-E site on carrier 802.11ac wireless M.2 Key-E site on carrier
Ethernet10/100/1000 BASE-T EthernetGigabit Ethernet 10/100/1000 BASE-T Ethernet
USB(4x) USB 3.0 + Micro-USB 2.01x USB 3.0 A | 2x USB 2.0 A | USB 2.0 Micro-B (3x) USB 3.1 + (4x) USB 2.0
PCIePCIe Gen 2 x1/x2/x4PCIe Gen 4 x16 | 1×8 + 1×4 + 1×2 + 2×1
CANNANADual CAN bus controller
Misc IOUART, SPI, I2C, I2S, GPIOsUART, SPI, I2C, I2S, GPIOs UART, SPI, I2C, I2S, GPIOs
Socket260-pin edge connector, 45x70mm260-pin edge connector, 45x70mm 699-pin board-to-board connector, 100x87mm
Thermals-25°C to 80°C -25°C to 80°C -25°C to 80°C
Power5/10W 5V / 5-10W 10/15/30W

Price Comparison Jetson Nano vs AGX Xavier

Jetson Nano 2GB
5%
Jetson Nano 4GB
9%
AGX Xavier
69%

Port Comparison

Jetson Nano 4GBJetson Nano 2GBAGX Xavier
MicroSD Card SlotMicroSD Card SlotMicroSD Card Slot
40-pin GPIO Expansion Header40-pin GPIO Expansion Header40-pin GPIO Expansion Header
Micro USB BMicro USB BUSB C
Gigabit Ethernet PortGigabit Ethernet PortGigabit Ethernet Port
4x USB 3.0 A1x USB 3.0 A | 2x USB 2.0 A(3x) USB 3.1 + (4x) USB 2.0
HDMI | Display PortHDMIHDMI 2.0
DC Barrel Jack Power InputNA DC Barrel Jack Power Input
2x MIPI CSI1x MIPI CSI16x MIPI CSI-2 

GPIO AGX Xavier

Connector LabelPinPinConnector Label
3.3 VDC125.0 VDC
I2C_GP5_DAT345.0 VDC
I2C_GP5_CLK56GND
MCLK0578UART1_TX
GND910UART1_RX
UART1_RTS1112I2S2_CLK
PWM011314GND
GPIO27_PWM21516GPIO8
3.3 VDC1718GPIO35_PWM3
SPI1_MOSI1920GND
SPI1_MISO2122GPIO17
SPI1_SCLK2324SPI1_CS0
GND2526SPI1_CS1
I2C_GP2_DAT2728I2C_GP2_CLK
CAN0_DIN2930GND
CAN0_DOUT3132GPIO9_CAN1
CAN1_DOUT3334GND
I2S_FS3536UART1_CTS
CAN1_DIN3738I2S_SDIN
GND3940I2S_SDOUT
Connector LabelPinPin Connector Label
GPIO AGX Xavier

GPIO Jetson Nano

Connector LabelPinPin Connector Label
3.3 VDC125.0 VDC
I2C_2_SDA 345.0 VDC
I2C_2_SCL 56GND
AUDIO_MCLK78UART_2_TX 
GND910UART_2_RX 
UART_2_RTS1112I2S_4_SCLK
SPI_2_SCK1314GND
LCD_TE1516SPI_2_CS1
3.3 VDC Power1718SPI_2_CS0
SPI_1_MOSI1920GND
SPI_1_MISO2122SPI_2_MISO
SPI_1_SCK2324SPI_1_CS0
GND2526SPI_1_CS1
I2C_1_SDA2728I2C_1_SCL
CAM_AF_EN2930GND
GPIO_PZ03132LCD_BL_PWM
GPIO_PE63334GND
I2S_4_LRCK3536UART_2_CTS
SPI_2_MOSI3738I2S_4_SDIN
GND3940I2S_4_SDOUT
Connector LabelPinPinConnector Label
GPIO Jetson Nano

Phoronix Test Suite :

So I started with some of the standard tests of the Phoronix Test Suite. You can also run the same test by executing the below commands.

sudo apt-get install -y php-cli php-xml

# Download PTS  https://www.phoronix-test-suite.com/ and  Install

phoronix-test-suite benchmark 1809111-RA-ARMLINUX005

# Accept for Dependency Installation and Wait a few hours, Test Result is available at : 

~/.phoronix-test-suite/test-results/
TestJetson Nano 4GBAGX XavierNotes
Tinymembench (memcpy)35016103.9MB/s, More Is Better
TTSIOD 3D Renderer4173.29FPS, More Is Better
7-Zip Compression350110219MIPS, More Is Better
C-Ray932328.39Seconds, Lower Is Better
Primesieve468158.96Seconds, Lower Is Better
AOBench187161.61Seconds, Lower Is Better
FLAC Audio Encoding104.0190Seconds, Lower Is Better
LAME MP3 Encoding144.2160.99Seconds, Lower Is Better
Perl (Pod2html)0.71140.5707Seconds, Lower Is Better
Redis (GET)568431680922Seconds, Lower Is Better
PyBench70806359Milliseconds, Lower Is Better
Categories: BenchmarkNvidia

Crazy Engineer

MAKER - ENGINEER - YOUTUBER

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