gpu

A bash-first, no-panic Linux GPU troubleshooting guide to fix black screens, crashes, and low FPS: install a CLI toolbox; inventory hardware and active drivers; read kernel logs and verify firmware; repair NVIDIA/AMD/Intel stacks with apt/dnf/zypper; validate OpenGL/Vulkan; monitor temps/usage; apply fixes (Secure Boot, DKMS/akmods, blacklist nouveau, hybrid offload, Wayland vs Xorg); plus quick wins and what logs to share.

A practical, bash-first blueprint for making a Linux workstation AI-ready: choose balanced hardware (GPU/VRAM, CPU, RAM, NVMe), install base dev tools, set up NVIDIA CUDA or AMD ROCm via distro-specific commands, create isolated Python envs and install PyTorch/TensorFlow/JAX, enable reproducible containers (Docker/Podman + NVIDIA toolkit), validate with llama.cpp and vLLM, and use the troubleshooting tips to maximize performance and longevity.

Pragmatic guide to building a private, low-latency AI GPU homelab on Linux: plan hardware (VRAM, PCIe, power, cooling; NVIDIA/AMD/Intel trade-offs), install drivers/toolkits, set up Docker/Podman with GPU access, and run real workloads (Ollama, llama.cpp, Stable Diffusion, PyTorch). Includes commands, monitoring/optimization (nvidia-smi/rocm-smi, power limits), container tips, troubleshooting, and next steps.

Hands-on guide to building a private, reproducible AI homelab on Linux: prep the OS, use Docker/Podman (GPU optional) to run local LLMs with Ollama, add search via Qdrant and a tiny Python RAG script, and transcribe audio with faster-whisper; includes hardware picks, cross-distro install steps, Compose for persistence, updates/monitoring, and troubleshooting—CPU-first, upgradable later.