edge ai

AI hardware lands on Linux first, but drivers, kernels and runtimes are fragmented. This guide shows why Linux is the hub for GPUs/NPUs and how to prepare with Bash: inventory hardware, set up compilers/Python/containers, install/verify CUDA, ROCm and OpenVINO, enable GPU access in Docker/Podman, monitor/benchmark, and stay current with kernels—so your workloads port smoothly to new accelerators.

Practical, Linux-first guide showing how the right hardware, not just FLOPs, cuts latency, cost, and power. Through three copy-paste case studies (CPU ONNX Runtime with NUMA/thread tuning, single-GPU PyTorch LoRA with 4/8-bit, and portable llama.cpp C++ inference), it gives install commands, monitoring tips (sensors, nvtop), repeatable benchmarks, and advice on topology, quantization, and clean environments.

Hands-on guide to run AI privately on your home lab: launch a local LLM API (Ollama or llama.cpp), build an offline voice assistant (Vosk STT + espeak TTS), detect SSH log anomalies with IsolationForest, and add private RAG search (sentence-transformers + Chroma). Provides apt/dnf/zypper commands, Bash/Python snippets, and ops tips for hardware, services, storage, and security.

Turn a spare Linux mini PC into a private, low-latency AI workstation with four hands-on builds: a local Ollama chatbot, real-time YOLO webcam detection, an offline Vosk + espeak-ng voice assistant, and CLIP-based photo tagging. Includes system prep, Python venvs, copy-paste commands, optional GPU/TPU, systemd autostart, and performance tips—no cloud bills, no data leaks.

A hands-on guide to hardening an always-on Raspberry Pi and using on-device AI for defense: apply a quick security baseline (updates, SSH keys, firewall, Fail2ban, unattended upgrades), then build a TensorFlow Lite “AI camera sentinel” for local person detection or run Frigate NVR in Docker (with/without Coral). Covers sandboxing, least-privilege, egress control, backups, and troubleshooting.

Hands-on Edge AI for Linux: explains benefits (lower latency, privacy, cost control), shows cross-distro setup (apt/dnf/zypper, Python venv), then builds three weekend projects—webcam image classification with ONNX Runtime, offline wake word via Vosk, and MQTT sensor anomaly alerts—with ready-to-run code, test commands, optional GPU/OpenVINO acceleration, systemd services, and optimization/deployment tips.

Learn how to run open, local AI code models on Linux to supercharge Bash: why they’re private, fast, and cost-predictable; how to prep your system; pick Ollama (quick HTTP API) or llama.cpp (OpenAI-style server); pull 7B-13B models; add ai(), ai-review(), ai-patch() helpers; use them for script scaffolds, log explanations, diffs, and refactors; plus tuning, safety, and GPU tips - offline and in control.

Local AI isn’t inherently safe. This guide hardens Linux on‑device models with practical, cross‑distro steps: verify downloads (checksums/GPG), sandbox file access (firejail/bubblewrap), block egress by default (sandbox flags or nftables + dedicated user), use rootless Podman with read‑only mounts and dropped caps, and sanitize/encrypt data—yielding safe‑by‑default local workflows.

Run AI where your data lives: this Bash-first Linux guide shows how to benchmark models with ONNX Runtime, containerize a minimal Python service in rootless Podman, decouple I/O via MQTT, and survive reboots with systemd. It includes apt/dnf/zypper commands, security hardening, resource limits, quantization tips, and real-world patterns for fast, private, resilient edge inference on small devices.

Hands-on homelab guide to AI on K3s: set up a tiny, multi-arch Kubernetes with Helm and MetalLB, then ship CPU-friendly Triton ONNX image classification, a llama.cpp LLM via GGUF, and full metrics with Prometheus+Grafana. Includes copy-paste manifests, curl tests, LAN LoadBalancers, plus GPU/multi-node tips and troubleshooting—portable, reproducible, and fast on edge hardware.

A hands-on guide to making local LLMs feel fast: why inference is memory/bandwidth bound, and 3–5 practical tweaks—pick the right quantised GGUF model, compile with OpenBLAS, pin threads and respect NUMA, right-size context, tune THP/swap, and set a performance governor—complete with apt/dnf/zypper installs, llama.cpp builds, monitoring tips, and real-world gains of 1.5–2× tokens/sec.