NVIDIA AI IOT
Official@nvidia-ai-iot · Santa Clara, California
Provides specialized configuration, deployment, and hardware-level optimization for NVIDIA Jetson edge computing platforms and embedded Linux BSP environments.
Agent Skills by NVIDIA AI IOT
Showing 35 vetted skills indexed across 3 GitHub repositories.
NVIDIA-AI-IOTjetson-customize-fan
Manage nvfancontrol fan profiles on NVIDIA Jetson/Tegra platforms.
NVIDIA-AI-IOTjetson-promote-image
Promote overlay files and built artifacts into staged Jetson Linux BSP images.
NVIDIA-AI-IOTjetson-link-docs
Register Jetson reference documents into an active profile using YAML and regex.
NVIDIA-AI-IOTjetson-generate-kb
Generate a knowledge base markdown file from a Jetson BSP and source tree layout.
NVIDIA-AI-IOTjetson-customize-clocks
Customize Jetson clock settings by editing BPMP DTB and nvpower.sh files.
NVIDIA-AI-IOTjetson-init-image
Extract Jetson Linux BSP tarballs and apply binaires.sh for target platforms.
NVIDIA-AI-IOTjetson-customize-nvpmodel
Modify nvpmodel power modes on Jetson/Tegra platforms.
NVIDIA-AI-IOTjetson-flash-image
Flash Jetson Linux BSP images on devices in recovery mode.
NVIDIA-AI-IOTjetson-print-bsp-info
Print L4T version, board configurations, and rootfs state from a Linux_for_Tegra directory.
NVIDIA-AI-IOTjetson-customize-usb
Configure USB2/USB3 SS ports and GPIOs via kernel-DTB overlay on Jetson platforms.
NVIDIA-AI-IOTjetson-set-target
Modify the active Jetson target pointer in active_target.yml.
NVIDIA-AI-IOTjetson-download-bsp
Automate downloading NVIDIA Jetson Linux BSP artifacts for the active target platform.
NVIDIA-AI-IOTjetson-customize-mgbe
Generate kernel device-tree overlays to configure MGBE QSFP interfaces on Jetson Thor boards.
NVIDIA-AI-IOTjetson-customize-camera
Generate kernel-DT overlays to enable camera sensors on NVIDIA Jetson platforms.
NVIDIA-AI-IOTjetson-derive-carrier
Fork reference files and create a Device Tree overlay for custom carrier boards.
NVIDIA-AI-IOTjetson-customize-pinmux
Configure Jetson Orin or Thor pinmux settings from XLSM data.
NVIDIA-AI-IOTjetson-customize-uphy
Configure UPHY lane allocation on NVIDIA Jetson custom carriers using ODMDATA tokens.
NVIDIA-AI-IOTjetson-init-target
Create a Jetson target-platform profile YAML from a reference devkit.
NVIDIA-AI-IOTjetson-build-source
Automate building Jetson BSP artifacts like DTBs, OOT modules, and kernel images.
NVIDIA-AI-IOTjetson-customize-pcie
Generate kernel-DT overlay fragments to configure PCIe controllers on Jetson carrier boards.
NVIDIA-AI-IOTjetson-init-source
Initialize the Jetson BSP source workspace with kernel sources and cross-compile toolchain.
NVIDIA-AI-IOTjetson-validate-image
Validates Jetson BSP image integrity and functionality after flashing via static checks and on-target tests.
NVIDIA-AI-IOTjetson-optimize-memory
Disable unused subsystems to reclaim DRAM in Jetson deployments.
NVIDIA-AI-IOTjetson-quick-start
Dispatch Jetson BSP setup skills based on questionnaire responses.
Frequently Asked Questions About NVIDIA AI IOT
FAQPage SchemaWhat specific hardware tasks are enabled by these capabilities?▼
These capabilities enable low-level hardware configuration including custom pinmux settings, UPHY lane allocation, PCIe controller setup, and thermal management via fan profiles. Users can also perform full BSP image flashing, kernel device-tree overlay generation, and DRAM memory optimization for resource-constrained edge deployments.
Which engineering personas benefit from these technical resources?▼
Embedded systems engineers, hardware integration specialists, and edge computing architects benefit from these resources. They are designed for professionals managing NVIDIA Jetson Orin or Thor platforms who require granular control over board support packages, kernel-level configurations, and hardware-accelerated inference serving.
What are the primary prerequisites for utilizing these deployment capabilities?▼
Users require an active NVIDIA Jetson development kit or custom carrier board, a Linux host environment for cross-compilation, and the L4T (Linux for Tegra) source tree. Access to specific board documentation and XLSM pinmux data is necessary for custom hardware integration tasks.