DEFONEOS × NVIDIA

Rubin → Blackwell → Hopper → Ada Lovelace. Isaac → Omniverse → TensorRT → CUDA. From Jetson edge to H100 cloud — the sovereign NVIDIA stack for defence AI.

The Full NVIDIA Stack in DEFONEOS

DEFONEOS integrates across the entire NVIDIA hardware and software stack — from $500 edge devices to H100 cloud clusters. The architecture is hardware-agnostic (DEFONEOS runs on CPU, Apple Silicon, AMD, and NVIDIA), but NVIDIA provides the highest performance acceleration for defence AI workloads.

Edge → Fog → Cloud Architecture

EDGE: Jetson Orin Nano (40 TOPS, $500, 15W)

The forward edge: drone-mounted, vehicle-mounted, or man-portable. Runs quantized models for real-time intelligence:

Power: 15W (USB-C battery). Weight: 150g. Operating temp: -25°C to +80°C. Air-gapped mode: Wi-Fi off, data stays on device, SIGIL receipts queued for sync.

FOG: Jetson AGX Orin (275 TOPS, 60W, $2,000)

The forward operating base compute layer. Runs a full SOV3 instance:

• Full 30 MCP server stack (lightweight configs)
• BFT council (33 agents on-device)
• SIGIL chain (local, synced when connected)
• 8B parameter models (deepseek-r1:8b at 20 tok/s)
• DeepStream video analytics (8x RTSP cameras simultaneously)
• Local Cesium 3D globe (offline terrain tiles cached)

Power: 60W (vehicle power or generator). Weight: 900g. Storage: 2TB NVMe. Network: 10GbE + Wi-Fi 6 + 5G modem.

CLOUD: H100 / B200 Cluster

The strategic compute layer. Heavy training, large-scale inference, federation coordination:

TensorRT Optimisation Results

DEFONEOS uses TensorRT to optimise all inference workloads on NVIDIA hardware. Benchmark results (L4 GPU):

Isaac Sim + DEFONEOS Swarm Pipeline

# DEFONEOS swarm training pipeline using NVIDIA Isaac Sim

1. DEFINE MISSION
   → DEFONEOS BFT council approves mission parameters
   → SIGIL emitted: "M|defoneos|swarm|Reconnaissance mission sector-7 approved"

2. SIMULATE IN ISAAC SIM
   → 10,000 drone agents in photorealistic Yorkshire terrain
   → NVIDIA PhysX physics simulation
   → Wind, weather, EM interference modelled
   → 1000 mission runs in 4 hours (H100 cluster)

3. TRAIN POLICY
   → PPO / SAC reinforcement learning
   → Curriculum: easy → hard scenarios
   → Transfer to real hardware via Isaac ROS

4. DEPLOY TO EDGE
   → Trained policy → Jetson Orin Nano (40 TOPS)
   → TensorRT INT8 quantization
   → On-device inference at 120 FPS

5. OPERATIONAL FEEDBACK
   → Real-world telemetry → SOV3 OLM
   → Online learning adjusts policy
   → Updated weights deployed via NIM containers
   → Full SIGIL chain for audit

Omniverse Digital Twin Integration

DEFONEOS exports its Cesium 3D globe scenes to NVIDIA Omniverse for photorealistic digital twin rendering. This enables:

• High-fidelity simulation: Photorealistic Yorkshire terrain with accurate lighting, weather, and physics for mission rehearsal
• Real-time bidirectional sync: Changes in DEFONEOS (new sensor data, threat detections) appear instantly in Omniverse
• Collaborative planning: Multiple users in VR/AR headsets reviewing the same digital twin simultaneously
• Synthetic data generation: Omniverse generates synthetic training data for computer vision models (varied lighting, weather, angles)

Sovereign Deployment Considerations

Air-Gapped NVIDIA Cluster

For classified environments (SECRET+), DEFONEOS can deploy a fully air-gapped NVIDIA cluster:

• DGX H100 rack (4–8 GPUs) with no network connection
• Software pre-loaded via one-way data diode (Thunderbolt → optical → host)
• Model weights transferred via encrypted USB / hardware token
• SIGIL receipts queued locally, synced when reconnected
• No external API calls — all 188+ tools run on local models

Apple Silicon vs NVIDIA Comparison

DEFONEOS is hardware-agnostic. Here's how Apple Silicon compares to NVIDIA for key workloads:

Conclusion: Apple Silicon excels at edge deployment (battery, portability, MLX). NVIDIA excels at training and high-throughput inference. DEFONEOS uses both — M4 Mac for tactical edge, NVIDIA for cloud training.

NVIDIA NIM Container Packaging

Each DEFONEOS MCP server is packaged as an NVIDIA NIM container for one-command deployment:

# Deploy the maritime ISR MCP as a NIM container
docker run --gpus all -p 8101:8101 \
  -e NVIDIA_VISIBLE_DEVICES=all \
  nvcr.io/csoai/defoneos/maritime-isr-mcp:latest

# Deploy the full 30-MCP fleet via Docker Compose
docker-compose -f defoneos-nim-fleet.yaml up -d

# Verify deployment
curl http://localhost:3101/mcp -d '{"jsonrpc":"2.0","method":"tools/list","id":1}'
# → 188 tools across 30 servers, all TensorRT-optimised

Roadmap