Solution Architecture

Local Intelligence Stack

Precision-Optimized Edge Inference for Sovereign Infrastructure

Run standard inference locally and escalate only high-order logic to cloud reasoning.

Preserve sensitive data on-prem while controlling latency and infrastructure spend.

Core Objective

Deliver high-performance, low-latency AI execution at the edge by aligning specialized model architectures with hardware-specific precision formats.

This stack minimizes unnecessary cloud round-trips while keeping data sovereign and infrastructure costs predictable.

Tier	Primary Use Case	Edge Models	Precision
Ultra-Fast	Real-time Vision & Tracking	YOLOv8 / Qwen3-VL 4B	INT4 / TensorRT
Responsive	Edge Agents & Automation	Gemma 4-2B / Qwen 0.8B	INT4 / W4A16
Balanced	Structured Reasoning	Ministral 3B / Gemma 4B	AWQ
High-Cap	Complex Local Inference	Qwen3-8B / Ministral 8B	INT4 / FP8

We employ a Local-First hybrid routing mechanism to balance intelligence and efficiency.

Perception Layer: Local nodes ingest multimodal data via <4B parameter models.
Controller Layer: Task complexity is evaluated on-device to determine the execution path.
Execution Path - Standard Operations: Processed locally at less than 20ms latency.
Execution Path - Advanced Reasoning: Conditionally routed to cloud-based LLMs for high-order logic.
Synthesis: Final results are returned to the edge node for secure, local action execution.

Intelligence at the edge is not defined by the size of the model, but by the precision of its execution.

Technical Specifications

The Foundation for Sovereign Edge Intelligence

Up to 70 TOPS in a compact, power-efficient module for local AI execution.

Primary sovereign node for autonomous agents and on-device LLM workloads.

Component	Specification
AI Performance	67 TOPS
GPU	NVIDIA Ampere architecture with 1024 CUDA cores and 32 Tensor cores
CPU	6-core Arm® Cortex®-A78AE v8.2 64-bit CPU 1.5MB L2 + 4MB L3
Memory	8GB 128-bit LPDDR5 102 GB/s
Storage	Supports SD card slot and 256GB to 1TB external NVMe
Video Encode	1080p30 supported by 1-2 CPU cores
Video Decode	1x 4K60 (H.265), 2x 4K30 (H.265), 5x 1080p60 (H.265),11x 1080p30 (H.265)

INT8 / INT4: Primary formats for real-time vision (YOLO) and edge LLMs (Gemma/Qwen).
FP16: Used for high-fidelity multimodal perception where precision is critical.
W4A16 (AWQ): Optimized for structured reasoning agents to balance memory and logic.

Networking: 1x GbE, M.2 Key E (Wi-Fi/BT)
Expansion Header: 40-pin Header (GPIO, I2C, I2S, SPI, UART)
Enables direct hardware control for sensors, actuators, and custom agent triggers.
Camera: 2x MIPI CSI-2 22-pin lanes (Virtual Channel Support)
Display: 1x DisplayPort 1.2
USB: 4x USB 3.2 Gen 2 (10 Gbps), 1x USB 2.0 (Micro-AB)
Mechanical: 69.6mm x 45mm, 260-pin SO-DIMM connector
Other I/O: 12-pin header for Power, Reset, and Force Recovery

Voltage Input: 9V to 20V
Power Profiles: 7W–25W (software defined)
Super Mode: Configurable to 70 TOPS peak performance for heavy local inference tasks

NVIDIA JetPack: Support for version 6.2/6.2.2 (Linux Kernel 5.15, Ubuntu 22.04)
AI compute stack: CUDA 12.x, TensorRT, cuDNN,
Frameworks: PyTorch, TensorFlow, ONNX Runtime, and NVIDIA Triton Inference Server
Agent Infrastructure: Native compatibility with OpenClaw deployment layers

Same Jetson module — different system behavior.

Dev boards = accessibility (USB, HDMI, quick bring-up)
Deployment boards = capability (industrial I/O, power stability, 24/7 reliability)

Key Shift:
Prototype → Product requires changes in
I/O • Power • Thermal • Interfaces

Core Insight:
The module runs AI, but the carrier board decides how AI interacts with the real world.