Solution Architecture

Local Intelligence Stack

Precision-Optimized Edge Inference for Sovereign Infrastructure

Local-First Routing

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

Sovereign Operations

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.

Performance Matrix

Tier Primary Use Case Recommended Models Precision
Ultra-Fast Real-time Vision & Tracking YOLOv8 / Qwen3 -VL 4B INT4 / TensorRT
Responsive Edge Agents & Automation Gemma 4 26B–A4B / Qwen 0.8B–3B INT4 / W4A16
Balanced Structured Reasoning Gemma 4 31B / Qwen3 32B AWQ
High-Cap Complex Local Inference Qwen3.5 27B / Nemotron Nano 30B–A3B INT4 / FP8
Max-Cap Advanced Reasoning (Hybrid) Qwen3.5 35B–A3B (MoE) AWQ
Cloud-Offload Large-Scale LLM Processing GPT OSS 20B / GPT OSS 120B AWQ

Execution Workflow (AGX Orin 64GB Optimized)

We implement a tier-aware local-first execution pipeline, dynamically balancing latency, memory, and model capacity.

  • Perception Layer: Multimodal inputs (vision, text, streams) are processed locally using ultra-fast models (≤4B) for real-time ingestion and feature extraction.
  • Controller Layer: On-device scheduler evaluates task complexity, latency constraints, and memory footprint, routing workloads across execution tiers.
  • Execution Path – Ultra-Fast: Real-time inference (<20ms) using lightweight CV + VLM models for tracking, detection, and immediate response.
  • Execution Path – Responsive / Balanced: Mid-tier LLMs (8B–32B class) execute structured reasoning and edge automation directly on-device using optimized inference.
  • Execution Path – High-Cap: Large models (30B+ class) run locally with quantization and memory-aware scheduling, enabling advanced reasoning within edge constraints.
  • Execution Path – Hybrid / Cloud: Tasks exceeding local compute limits (>50B or long-context reasoning) are selectively offloaded to cloud LLMs.
  • Synthesis Layer: Outputs are consolidated at the edge for secure, low-latency action execution, maintaining deterministic system behavior.

Technical Implementation

Target Hardware: NVIDIA Jetson Orin Nano (8GB / 70 TOPS)

Software Stack:

  • Quantization: TensorRT-LLM / AutoAWQ
  • Deployment: OpenClaw Sovereign Node
  • Runtime: JetPack 6.x / Triton Inference Server

Core Insight

Jetson AGX Orin 64GB enables true multi-tier AI orchestration — where real-time perception, mid-scale reasoning, and selective cloud intelligence operate as a single unified system.
Technical Specifications:

NVIDIA Jetson AGX Orin (32GB||64GB)

The Foundation for Sovereign Edge Intelligence

Performance Envelope

Up to 275 TOPS in a compact, power-efficient module for local AI execution.

Enterprise Role

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

Core Performance Architecture

Component Specification
AI Performance 200 TOPS || 275 TOPS
GPU 56 Tensor Cores 930MHz 1792-core NVIDIA Ampere architecture GPU || 64 Tensor Core 2048-core NVIDIA Ampere architecture GPU
CPU 56 Tensor Cores 930MHz 1792-core NVIDIA Ampere architecture GPU || 64 Tensor Core 2048-core NVIDIA Ampere architecture GPU
Memory 32GB 256-bit LPDDR5 204.8GB/s || 64GB 256-bitLPDDR5 204.8GB/s
Storage 256 GB NVME to 4 TB upgradeable
Video Encode 1x 4K60 (H.265)| 3x 4K30(H.265) | 6x 1080p60(H.265) | 12x 1080p30 (H.265), 2x 4K60(H.265) | 4x 4K30(H.265) | 8x 1080p60(H.265) | 16x 1080p30 (H.265)
Video Decode 1x 8K30(H.265) | 2x 4K60(H.265) | 4x 4K30 | 9x 1080p60| 18x 1080p30 (H.265), 1x 8K30(H.265) |3x 4K60(H.265) | 7x4K30(H.265) | 11x 1080p60(H.265) | 22x 1080p30 (H.265)

Precision-Aware Inference Capabilities

  • 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.

Connectivity & I/O Expansion

  • CAN BUS: 2x CAN (Varies on deployment carrier board)
  • Networking: 1x RJ45 Gigabit Ethernet port, 1x RJ45 Gigabit Ethernet port(Note:Varies on deployment carrier board)
  • Camera: Up to 6 cameras (16 via virtual channels) 16 lanes MIPI CSI-2 D-PHY 2.1 (up to 40Gbps) | C-PHY 2.0 (up to 164Gbps)
  • Display: 1x DisplayPort 1.2
  • USB: 2x USB 3.0 Type-A
  • M.2 Port:1x M.2 Key M (PCIe NVME 2280 SSD)
  • GPIO:4x UART, 3x SPI, 4x I2S, 8x I2C, 2x CAN, PWM, DMIC & DSPK, GPIOs
  • Dimensions: 133mm × 131mm × 51mm

Power & Thermal Management

  • Voltage Input: 19–24V DC
  • Operating Temperature: -20℃ ~ 60℃

Software & Ecosystem Support

  • NVIDIA JetPack: Support for version 6.x (Linux Kernel 5.15, Ubuntu 22.04)
  • Libraries: CUDA 12.x, TensorRT, cuDNN, OpenCV
  • Agent Infrastructure: Native compatibility with OpenClaw and SetupClaw deployment layers

⚠️ Carrier Board Insight

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.