AI
Run inference, fine-tune models, and build AI-powered infrastructure tools — all on ZFS-backed storage with NVIDIA GPU passthrough. These guides cover the full stack from local LLM hosting to AI-driven operations.
Getting Started
Getting Started
Set up your first AI workstation on kldload — NVIDIA drivers, CUDA, and local LLM inference with Ollama or vLLM.
Train on Your Infra
Fine-tune models on your own infrastructure data — logs, metrics, and configurations become training material.
Voice & Vision
Speech-to-text, text-to-speech, and computer vision pipelines running locally on GPU with ZFS dataset management.
AI for Infrastructure
AI for ZFS
AI-assisted ZFS management — anomaly detection on pool health, predictive scrub scheduling, and smart compression tuning.
AI for eBPF
Combine eBPF observability with AI analysis — automated root cause detection and intelligent alerting from kernel traces.
AI for WireGuard
AI-driven network management — traffic pattern analysis, tunnel health prediction, and automated peer configuration.
AI for Docker
AI-optimized container workflows — image layer analysis, resource prediction, and intelligent scheduling on ZFS.
AI for KVM
AI-managed virtualization — VM right-sizing, migration planning, and GPU resource allocation across KVM hosts.
AI for Kubernetes
AI-enhanced Kubernetes operations — cluster autoscaling, pod placement optimization, and predictive failure detection.