**This is not a duplicate listing. I just have two of them.**

This Kit comes with

- Sata Cables for additional drive storage.

- WiFi/bluetooth card 802.11a/b/g/n/ac

- 256GB SD card Storage

- 3D printed housing

- Power Cable adapter

The NVIDIA Jetson TK1 Software Development Kit features the NVIDIA Jetson TK1 model equipped with the Tegra K1 embedded GPU. This kit is designed for software development purposes, offering a powerful platform for creating applications and projects that require GPU acceleration. It is an ideal choice for developers working on advanced computing and AI projects.

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NVIDIA Jetson TK1 - Full Specifications

Processor (SoC)

• Model: NVIDIA Tegra K1

• CPU: 4+1 ARM Cortex-A15 (quad-core + power-saving core)

• Clock Speed: Up to 2.3 GHz

GPU

• Model: NVIDIA Kepler GPU with 192 CUDA cores

• Architecture: Kepler

• Support: OpenGL 4.4, OpenGL ES 3.1, CUDA 6.0, DirectX 11, Vulkan (limited)

Memory (RAM)

• Size: 2GB DDR3L

• Clock Speed: 933 MHz

• Memory Interface: 64-bit

• Bandwidth: 14.9 GB/s

Storage

• Internal Storage: 16GB eMMC 4.5

• Expandable Storage:

• microSD card slot

• SATA 2.0 (via expansion port)

Operating System

• Default OS: Ubuntu 14.04 LTS with L4T (Linux for Tegra)

• Supported OS Options: Ubuntu-based distros, custom Linux builds

Connectivity

• Ethernet: Gigabit Ethernet (10/100/1000 Mbps)

• Wi-Fi: Not built-in (requires external module)

• Bluetooth: Not built-in (requires external module)

I/O Ports

• USB:

• 1x USB 3.0 (Host)

• 1x USB 2.0 Micro-AB (OTG)

• HDMI: 1x HDMI 1.4a output

• SATA: 1x SATA 2.0 (requires additional power)

• PCIe: 1x PCIe Gen 2.0 (single lane)

• Audio: 3.5mm audio jack (stereo)

• Camera Interface: 1x MIPI CSI-2

Expansion and GPIO

• GPIO Pins: 7x GPIO (configurable)

• I2C Ports: 2x I2C

• SPI: 1x SPI

• UART: 2x UART

Power

• Power Supply: 5V, 2A (barrel jack)

• Power Consumption: ~5-15W (depending on workload)

Physical Dimensions

• Size: 5” x 5” (127mm x 127mm)

• Weight: ~100g

Use Cases

• Embedded AI and robotics

• Computer vision

• GPU-accelerated computing

• Edge computing and IoT applications