Private AI stack architecture
Reference blueprint from ingress to model serving to policy boundaries for on-prem and hybrid deployments.
AI Lab
Technical notes on how private AI platforms are designed, tuned, and operated for secure high-throughput delivery.
Observability that operators trust
Metrics, logs, traces, and GPU telemetry designed for signal quality and incident response speed.
GPU workload tuning
Practical benchmark-driven tuning across batching, memory pressure, queue depth, and I/O behavior.
Technical notes
- No sensitive client internals or private topology disclosures
- Repeatable implementation patterns tied to measurable outcomes
- Production-readiness checklists for architecture and rollout reviews
- Operator-first runbook and alerting design considerations