Bring up a Jetson TX2i

The NVIDIA Jetson TX2i is the industrial-grade variant of the Tegra X2 module: a hex-core ARM SoC (dual NVIDIA Denver + quad Cortex-A57) paired with a 256-core Pascal GPU. It is one of the supported targets, at the system-daemon support level, chosen for AI inference workloads where on-board GPU acceleration changes the data-reduction math.

NVIDIA Jetson TX2i: aarch64 with a Pascal GPU.

The Jetson developer carrier uses the standard firmware-and-root-filesystem flow. Custom carrier boards (Connect Tech, Auvidea, and similar) use the same flow with vendor-specific device-tree overlays.

Use the JetPack / L4T line that still supports TX2i in your vendor board support package. TX2i deployments commonly remain on JetPack 4.x because newer JetPack lines target later Jetson modules.

Hardware topology

• 2× NVIDIA Denver 2 cores + 4× ARM Cortex-A57.
• 256-core Pascal GPU with CUDA support and TensorRT runtime.
• 8 GB LPDDR4 memory, 32 GB eMMC, MIPI CSI camera lanes, multiple PCIe Gen 2 lanes.
• Industrial-temperature variant: −40 °C to +85 °C operating range, no soldered-on hardware that limits flight qualification.

Flash a Linux image

NVIDIA provides JetPack for Jetson modules: an SDK Manager bundling the board support package, Linux for Tegra (L4T), CUDA, cuDNN, and TensorRT.

Start from the vendor L4T image, then enable the target’s KVM, contiguous memory area, and GPU access settings in the target overlay.

Flash and configure the module:

1. Install NVIDIA SDK Manager on an x86_64 Linux host.
2. Place the TX2i module in recovery mode (jumper or button sequence; consult the carrier-board documentation).
3. Run SDK Manager and flash the OS image and components.
4. Apply the target overlay for KVM support, the contiguous memory area the kernel reserves for devices, and device access policy.

Boot and verify

After flashing, boot the module and log in with the credentials created during the L4T installation.

ls /dev/kvm                   # KVM enabled
nvidia-smi                    # GPU visible
cat /etc/nv_tegra_release     # L4T version

The Pascal GPU is reachable through CUDA from container services (isolation: runc) when the target grants the device nodes; unikernel access uses a host-mediated delegation path.

Selection criteria

Choose the TX2i when:

• Your workload is GPU-bound (typical AI inference: object detection, segmentation, classification).
• You can accept the +85 °C upper limit and the Pascal-era performance envelope.
• You need a relatively self-contained module (the carrier board is the only mission-specific hardware).

The VCK190 provides higher inference throughput on FPGA fabric. The Raspberry Pi 4B suits lower-power workloads that do not need acceleration.