Technical Architecture — Nemotron Integration

ONEANA × NEMOTRON

Sovereign AI Models for National Infrastructure

The full NVIDIA Nemotron 3 model family — Nano, Super, and Ultra — deployed across ONEANA's three-tier compute architecture. From the Jetson edge node in every patrol car to the DGX H100 cluster in Georgetown. Open weights. Sovereign compute. Zero foreign dependency.

3Model Tiers
NanoEdge — 3.6B Active
SuperDept — 10B Active
UltraFederal — 50B Active
1MToken Context
100%Open Weights
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Why Nemotron
Open Weights.
Sovereign by Design.
Built for Agents.

Most nations deploying AI depend on proprietary models hosted on foreign servers. A single API policy change can disable an entire national security apparatus overnight. ONEANA eliminates this risk entirely by running NVIDIA Nemotron 3 — fully open models with published weights, training recipes, and datasets — on sovereign hardware inside Guyana.

The Nemotron 3 family uses a hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture. This means only a fraction of the model's parameters activate per inference — 3.6B of 31.6B for Nano, 10B of ~100B for Super. The result: frontier-class reasoning at a fraction of the compute cost.

The native 1M-token context window gives agents long-term memory. A police investigation spanning weeks of evidence, a customs audit referencing months of trade data, a health surveillance system tracking an outbreak across regions — all fit in a single context.

Sovereignty Guarantee

Nemotron 3 is fully open: weights, datasets, and training recipes published on Hugging Face. If NVIDIA disappeared tomorrow, Guyana would still have complete, modifiable, production-ready AI models running on its own hardware. No API key. No license server. No kill switch.

MoE Efficiency

Mixture-of-Experts routing activates only relevant expert networks per token — why a 31.6B-parameter model runs on a Jetson Orin NX with 16GB memory.

11%
Nano Active Ratio
10%
Super Active Ratio
10%
Ultra Active Ratio
The Nemotron 3 Family
Three Models. Three Tiers. One Architecture.

Each model maps precisely to a tier in the ONEANA compute architecture. Nano runs on the edge. Super runs the departments. Ultra powers the federal layer.

Edge Tier
Nemotron 3 Nano
Hybrid Mamba-Transformer MoE
Compact enough for the Jetson Orin NX. Powerful enough for real-time LPR, weapon detection classification, natural language dispatch, and on-device reasoning. Runs entirely offline — no network required.
31.6B
Total Params
3.6B
Active / Token
1M
Context Window
Faster vs Prev Gen
ONEANA Use Cases
LPR plate read + watchlist match
Weapon detection classification
Natural language radio dispatch
Officer voice command interface
Border checkpoint ID verification
Ambulance triage assistant
Department Tier
Nemotron 3 Super
Latent MoE + Multi-Token Prediction
The departmental brain. Runs on the DGX H100 cluster as NIM microservices. Handles cross-department pattern detection, multi-step agentic reasoning, and real-time intelligence correlation across all 10 departments.
~100B
Total Params
10B
Active / Token
1M
Context Window
Throughput vs Prev
ONEANA Use Cases
Cross-dept pattern correlation
Predictive hotspot mapping
Multi-step investigation agents
Disease outbreak surveillance
Trade anomaly detection
NemoClaw secure agent runtime
Federal Tier
Nemotron 3 Ultra
Frontier Intelligence + NVFP4
Frontier-level reasoning for the President, Cabinet, and National Security Council. Multi-day investigation synthesis, national threat assessment, economic impact modeling, and strategic policy analysis.
~500B
Total Params
50B
Active / Token
1M
Context Window
NVFP4
4-Bit Precision
ONEANA Use Cases
National threat assessment
Multi-day investigation synthesis
Economic policy impact modeling
Cross-department strategic analysis
Resource allocation optimization
Cabinet briefing generation
Deployment Architecture
Three Tiers. Matched to Hardware.

Each Nemotron model deploys on the hardware tier it was designed for. No compromises. No overloaded nodes. Every layer runs at optimal throughput.

Tier 1 — Edge
380 × Jetson Orin NX — 16GB Unified Memory Each
Nemotron 3 Nano
Every patrol car, border checkpoint, fire truck, ambulance, and maritime vessel carries a Jetson Orin NX running Nemotron 3 Nano locally. The model fits in 16GB unified memory with the 3.6B active parameter MoE architecture. Inference happens on-device with zero network dependency — if satellite connectivity drops in the hinterland, the AI still works. Edge results stream to the DGX cluster when connectivity is available for aggregation and cross-department correlation.
380
Jetson Nodes
<50ms
Inference Latency
0
Network Required
TRT-LLM
Runtime
Ollama
Alt Runtime
LPR → Plate extract → Watchlist match
Camera → DeepStream → Nano classify
Voice → Nano NLU → Dispatch command
Tier 2 — Department
4 × DGX H100 Nodes — 32 PFLOPS Combined — Georgetown Data Centre
Nemotron 3 Super
Nemotron 3 Super runs as NVIDIA NIM microservices on the DGX H100 cluster. Each department's dashboard calls the same NIM API endpoint — one deployment serves all 10 departments. The model handles cross-department pattern detection, multi-step agentic reasoning chains, and real-time intelligence correlation. With NemoClaw, every agent action is logged, auditable, and constrained by privacy guardrails.
4
DGX H100 Nodes
32
PFLOPS
NIM
Deployment
10
Dept Consumers
NemoClaw
Agent Security
Edge alerts → Super correlate → Cross-dept intel
Query → Super agent → Multi-tool reasoning chain
Sensor stream → Super predict → Hotspot dispatch
Tier 3 — Federal
Dedicated DGX Partition — NVFP4 Precision — Classified Network
Nemotron 3 Ultra
Nemotron 3 Ultra runs on a dedicated partition of the DGX cluster, isolated on a classified network segment accessible only to the President, Cabinet, and National Security Council. NVFP4 4-bit precision delivers frontier-class reasoning at 5× throughput efficiency on Blackwell-compatible hardware.
~500B
Parameters
NVFP4
4-Bit Precision
Throughput Efficiency
Classified
Network Segment
Cabinet
Access Level
All dept data → Ultra synthesize → Threat assessment
Investigation corpus → Ultra reason → Strategic brief
National metrics → Ultra model → Policy recommendation
Inference Pipeline
Sensor to Decision. End to End.

A single data event flows through the full Nemotron stack — from edge detection to departmental reasoning to federal intelligence — in under 200ms.

Sensor Input
Camera / LPR / Acoustic / IoT
DeepStream
Video Analytics Pre-Process
Nano (Edge)
Classify & Extract — Jetson
Super (DGX)
Correlate & Reason — NIM
Dashboard
10 Dept Command Views
Example: Cross-Department Interdiction Flow
1
Edge — Nemotron Nano on Jetson
Patrol vehicle LPR camera captures plate. Nano extracts plate number in <50ms. Matches against local watchlist. Flags vehicle as stolen. Result streams to DGX cluster.
2
Department — Nemotron Super on DGX via NIM
Super agent receives alert. Queries customs database: vehicle linked to flagged cargo manifest. Queries immigration: driver entered Guyana 48hrs ago on expired visa. Queries police: outstanding warrant. Super generates multi-department alert with recommended actions.
3
Dispatch — Automated Multi-Department Response
Alert hits police dispatch dashboard (intercept unit), customs dashboard (cargo hold), immigration dashboard (border watch). Drone auto-dispatched for aerial tracking. All three departments see the same data, same moment, no phone calls needed.
4
Federal — Nemotron Ultra (If Escalated)
If the interdiction reveals a larger smuggling network, Ultra synthesizes months of related cargo data, immigration patterns, and police intelligence into a strategic threat assessment for the National Security Council.
Agentic AI Workflows
Nemotron Super + NemoClaw

Nemotron 3 Super was purpose-built for multi-agent tool-calling. Combined with NemoClaw's privacy and security guardrails, ONEANA runs autonomous AI agents that investigate, correlate, and recommend — with full audit trails.

AgentModelDepartmentsFunctionGuardrails
Patrol IntelNano (Edge)PoliceReal-time plate/face/weapon classification on patrol vehicleOn-device only. No PII transmitted raw.
Interdiction AgentSuper (NIM)Police + Customs + ImmigrationMulti-step cross-department investigation and alert generationNemoClaw audit log. Human approval for actions.
Outbreak WatchSuper (NIM)Health + EducationDisease pattern detection across hospitals and schoolsAnonymized health data. No patient PII in agent context.
Trade AnomalySuper (NIM)Customs + MaritimeCargo manifest analysis and maritime vessel correlationNemoClaw. Read-only database access.
Fire DispatchSuper (NIM)Fire + PoliceThermal detection correlation with nearest unit dispatchAuto-dispatch requires captain confirmation.
Resource OptimizerSuper (NIM)All 10 DepartmentsBudget and personnel allocation recommendationsRecommendation only. No auto-allocation.
Strategic AnalystUltra (Classified)Federal / NSCNational threat synthesis and policy impact modelingClassified network only. Cabinet clearance required.
Cabinet BrieferUltra (Classified)FederalAutomated daily intelligence briefing for head of stateHuman review before delivery. Full audit trail.
NemoClaw Runtime

Every Nemotron Super agent runs inside the NemoClaw secure runtime (announced GTC 2026). NemoClaw wraps the OpenClaw open-source agent platform with NVIDIA's privacy and security guardrails, ensuring every agent action is logged, constrained, and auditable.

Full Audit Logging Privacy Guardrails Action Constraints Human-in-the-Loop
Fine-Tuning Pipeline

Nemotron 3 models are fine-tuned on Guyanese data: legal codes, police radio transcripts, customs tariff schedules, health protocols, Creolese language patterns, and local geography. NVIDIA's published training recipes and Nemotron-Personas synthetic data framework enable privacy-preserving dataset generation.

Open Recipes Guyanese Legal Codes Creolese NLU Synthetic Data
Digital Sovereignty
No API Key. No License Server. No Kill Switch.

Nemotron 3's open-weight architecture means Guyana owns its AI the same way it owns its roads. The models run on domestic hardware, trained on domestic data, under domestic law.

Open Weights
All Nemotron 3 weights are published on Hugging Face. Guyana has the complete model — every layer, every parameter, every expert network. No obfuscation. No encrypted checkpoints. Full auditability from input to output.
Open Training Recipes
NVIDIA published the complete training pipeline. Guyana can retrain or fine-tune any model from scratch using domestic data. No dependency on NVIDIA's training infrastructure or proprietary tooling.
On-Premise Compute
All inference and training runs on DGX H100 hardware physically located in Georgetown. No cloud fallback. No foreign data centre. Every computation happens under Guyanese jurisdiction with AES-256 encryption at rest.
No API Dependency
Unlike nations running on GPT-4 or Claude API endpoints, ONEANA has no external API dependency. No rate limits imposed by a foreign company. No pricing changes. No service terms that could be modified unilaterally.
Edge Resilience
Nemotron Nano runs entirely on-device on 380 Jetson Orin NX units. Even if the Georgetown data centre goes offline, every patrol car, border post, and ambulance retains full AI capability.
Continuity Guarantee
If NVIDIA ceased to exist tomorrow, Guyana would still have: complete model weights, training recipes, inference runtimes (all open-source), and hardware already deployed. Zero single-vendor risk.
The Sovereign AI Principle

A nation's digital intelligence should be as sovereign as its territory. ONEANA + Nemotron 3 is the first implementation of this principle at national scale: open models, on domestic hardware, trained on domestic data, serving every level of government from the officer on the street to the head of state. No foreign entity has a kill switch. No API provider can throttle national security. The AI belongs to Guyana.

Implementation Roadmap
Four Steps to Sovereign AI

The Nemotron implementation follows a phased approach: deploy, fine-tune, activate agents, and optimize.

Step 1 — NIM Deployment
Deploy Nano & Super as NIM Containers
Flash Nano onto 380 Jetson Orin NX via TRT-LLM containerized deployment. Deploy Super as NIM microservices on DGX H100 cluster. Validate inference throughput and latency benchmarks.
Step 2 — Fine-Tuning
Train on Guyanese Data
Fine-tune Super on Guyanese legal codes, police transcripts, customs schedules, health protocols, and Creolese language. Generate synthetic training data via Nemotron-Personas framework.
Step 3 — Agent Activation
Deploy NemoClaw Agentic Runtime
Install NemoClaw on OpenClaw agent platform. Configure 8 department-specific agents with tool access, privacy guardrails, and audit logging. Human-in-the-loop approval for high-impact actions.
Step 4 — Ultra Activation
Federal Intelligence Layer
Deploy Nemotron 3 Ultra on dedicated classified DGX partition. Activate Strategic Analyst and Cabinet Briefer agents. Connect to all 10 department data streams. National Security Council validation and sign-off.