Executive Summary
The Platform is a production-grade infrastructure monitoring and visualization platform engineered to demonstrate enterprise-scale system architecture competencies. Built atop Ubuntu 26.04 with real-time kernel modifications (7.0.0-29-generic SMP PREEMPT_DYNAMIC), this platform orchestrates 44+ specialized AI/ML models within containerized microservices, providing comprehensive observability through physics-based network simulation [1].
System Architecture
Hardware Infrastructure
- Compute: AMD Ryzen 9 processor with NVIDIA GPU acceleration
- Virtualization: KVM-based hypervisor with hardware passthrough
- Network: Custom kernel networking stack (7.0.0-29-generic SMP PREEMPT_DYNAMIC)
- Storage: NVMe SSD array with software-defined storage (Ceph/vSAN)
Hardware Selection Rationale
Current Deployment: AMD Ryzen 9
Selected for optimal balance of performance, availability, and cost-effectiveness. The Ryzen 9 platform provides adequate compute capacity for infrastructure supporting 150-200 employees while maintaining consumer-grade accessibility and straightforward single-socket deployment.
Scalability Path: AMD EPYC / ThreadRipper
For heavier power-user infrastructures with increased computational demands, dual-processor EPYC or ThreadRipper configurations are available as an upgrade path. These enterprise-grade platforms offer expanded core counts, PCIe lanes, and memory channels for workloads exceeding current requirements.
Architecture Principle: Match hardware to actual workload requirements. Avoid over-engineering while maintaining clear scalability pathways.
Software Architecture
| Layer | Technology | Version / Specification | Status |
|---|---|---|---|
| Host OS | Ubuntu | 26.04 (Noble Numbat) | Active |
| Kernel | Custom Linux Kernel | 7.0.0-29 (SMP PREEMPT_DYNAMIC) | Active |
| Container Runtime | Docker + containerd | 29.5.2 CE | Active |
| Orchestration | Docker Compose | v5.1.4 (containerd) | Active |
| AI/ML Runtime | Ollama | 0.32.4 (CUDA enabled) | Active |
| Vector Database | Qdrant | 1.19.0 | Active |
| Monitoring | Prometheus + Grafana | Latest stable | Active |
| Automation | n8n | Latest | Active |
AI/ML Model Inventory
Production-deployed models optimized for edge inference via GGUF Q4_K_M quantization. Full inventory available via interactive dashboard [1].
| Model ID | Domain | Status |
|---|---|---|
| llama3:70b-instruct | General Instruction | Active |
| llama3.2:3b | Edge / Lightweight | Active |
| llama3.2-vision:11b | Vision-Language | Active |
| gemma4:31b | General | Active |
| gemma4:e4b | Edge Optimized | Active |
| gemma3:4b | General | Active |
| qwen3.6:35b | General | Active |
| qwen3.6:27b | General | Active |
| qwen3.5:9b | General | Active |
| qwen2.5-coder:7b | Code Generation | Active |
| granite4.1:30b | Enterprise | Active |
| granite3.3:8b | Enterprise | Active |
| phi4:latest | General | Active |
| glm-4.7-flash | General | Active |
| medgemma:27b | Medical | Active |
| meditron:7b | Medical | Active |
| med-english:latest | Medical | Active |
| bge-m3:latest | Embedding | Active |
| nomic-embed-text:v1.5 | Embedding | Active |
| whisper:latest | Audio / Speech | Active |
| + 24 additional models across vision, code, and edge domains | ||
Total Production Models: 44 across general, code, medical, vision, and embedding domains.
Security & Compliance
Implemented Controls
- Access Control: Role-based (RBAC) via Active Directory integration
- Network Segmentation: Docker network isolation, VLAN segmentation
- Data Encryption: LUKS at-rest, TLS 1.3 in-transit
- Monitoring: Real-time anomaly detection via Wazuh SIEM
- Backup/DR: Incremental rsync with versioned retention, automated cron schedules (RTO <4hr, RPO <24hr) [1]
Compliance Mapping
| Framework | Alignment | Status | Notes |
|---|---|---|---|
| HIPAA Technical Safeguards | Full Implementation | Compliant | Medical model deployment environment |
| NIST 800-53 | Moderate Baseline | Mapped | Security controls aligned |
| ISO 27001 | ISMS Requirements | Aligned | Information security management |
| DoD 8570 | IAT Level III | Compliant | Compliant per verbatim; formal audit pending |
Performance Specifications
| Metric | Specification | Target | Current |
|---|---|---|---|
| System Uptime | 99.9% | SLA | 99.97% |
| Inference Latency | <100ms | Per-query | 45ms avg |
| Concurrent Models | 44+ active | Capacity | 44 active |
| Data Throughput | 10 GbE upstream / 2.5 GbE host | Backbone | 10 GbE up / 2.5 GbE host |
| Recovery Time Objective | <4 hours | Critical | 2.5 hr |
| Recovery Point Objective | <15 minutes | All systems | 10 min |
Network Topology
┌─────────────────────────────────────────────────────────────┐ │ THE PLATFORM CONTROL PLANE │ ├─────────────────────────────────────────────────────────────┤ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ KERNEL │ │ OLLAMA │ │ DOCKER │ │ │ │ 7.0.0-29 │ │ SERVER │ │ ENGINE │ │ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ ┌──────┴────────────────┴────────────────┴──────┐ │ │ │ DOCKER COMPOSE (v5.1.4) │ │ │ └─────────────────────────┬─────────────────────────┘ │ │ │ │ │ ┌─────────────────────────┴─────────────────────────┐ │ │ │ CONTAINERIZED MICROSERVICES │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌────────┐ │ │ │ │ │ Grafana │ │ Open │ │ n8n │ │ Qdrant │ │ │ │ │ │+Prometheus│ WebUI │ │Automation│ │(Vector)│ │ │ │ │ └─────────┘ └─────────┘ └─────────┘ └────────┘ │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌────────┐ │ │ │ │ │ Wazuh │ │Watchtower│ │ Tika │ │PyTorch │ │ │ │ │ │ (SIEM) │ │(Auto-Up) │ │(Extract)│ │(Research)│ │ │ │ │ └─────────┘ └─────────┘ └─────────┘ └────────┘ │ │ │ └───────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────┘