In-progress lab development
AI Infrastructure Lab
The AI Infrastructure Lab is an in-progress design and expansion effort centered on GPU systems and Linux infrastructure, followed by Ceph storage, high-speed networking, telemetry, security, observability, and developing bare-metal automation and platform capabilities. Public material distinguishes target architecture from confirmed deployment.
Purpose
Turn an AI-infrastructure-first career direction into reviewable, production-minded evidence.
The target program starts with AI infrastructure and GPU operations, grounded in Linux, Ceph storage, high-speed networking, data-center deployment, telemetry, security, and observability. Kubernetes, provisioning, and automation remain active development areas. Each build increment must produce an honest artifact: an architecture decision, tested runbook, configuration, dashboard, recovery exercise, or technical write-up.
Target architecture
A planned topology for guiding staged implementation—not a claim of installed infrastructure.
Static architecture view · planned state
Target lab architecture
Dashed paths mark target integrations
Topology text description
Operator workflows feed a planned management plane for out-of-band control, bare-metal provisioning, and configuration. A central network fabric connects target Linux and Kubernetes compute nodes with planned distributed storage. Security controls and observability span every plane rather than operating as isolated add-ons.
- Management: target BMC, IPMI, Redfish, PXE, and automation workflows.
- Compute and storage: planned learning environments, not completed production claims.
- Cross-cutting controls: identity, hardening, telemetry, alerting, and operational reporting.
Compute plane
Target state: documented Kubernetes control-plane and worker roles, with future GPU enablement validated through repeatable health and scheduling checks.
Target state · Planned
Management plane
Target state: a controlled inventory, access, provisioning, and recovery layer for BMC, IPMI, Redfish, and host lifecycle practice.
Target state · Planned
Network fabric
Target state: separated management, storage, and workload traffic with a documented learning path toward higher-speed AI and storage fabrics. Addressing and hardware remain undisclosed.
Target state · Planned
Storage plane
Target state: Ceph-backed persistent storage with capacity, health, recovery, and Kubernetes integration evidence. Private inventory and topology are intentionally excluded.
Target state · Planned
Provisioning
Planned work covers PXE concepts, repeatable bare-metal bootstrap, host inventory, validation gates, and documented recovery paths.
Active development area
Observability
Planned work connects Prometheus and Grafana with host, storage, Kubernetes, and future GPU telemetry, then turns signals into actionable runbooks.
Active development area
Security model
Target controls include least-privilege administration, isolated management access, certificate lifecycle discipline, vulnerability review, hardening, and redacted evidence.
Active development area
Automation
Planned automation emphasizes Bash and Python workflows, infrastructure-as-code practice, declarative delivery, idempotent changes, and operator-readable failure modes.
Active development area
Current experiments
Selecting a first build slice that can produce verifiable evidence without implying that the target environment already exists.
Roadmap item
Learning roadmap
AI and GPU infrastructure concepts → Linux and bare-metal operations → Ceph storage and high-speed networking → security, telemetry, and observability → provisioning, orchestration, and platform automation.
Roadmap item
Future expansion
Potential expansion includes additional compute, storage, higher-speed networking, and GPU capacity. Models, quantities, costs, and timing remain intentionally unpublished.
Roadmap item