Linux

TL;DR Moving Ollama models to another drive requires changing the OLLAMA_MODELS environment variable and relocating your existing model files. By default, Ollama stores models in ~/.ollama/models on Linux systems, but you can point it to any directory with sufficient space. The fastest approach: stop the Ollama service, set OLLAMA_MODELS to your new location, move the existing models directory, then restart. For systemd-managed installations, edit /etc/systemd/system/ollama.service to add Environment=“OLLAMA_MODELS=/mnt/storage/ollama-models” under the [Service] section. After running systemctl daemon-reload and systemctl restart ollama, verify the new path with ollama list. ...

TL;DR When you switch between models in Ollama, the service unloads the current GGUF file from memory and loads the new one from disk. Large models like llama3.1:70b or mixtral:8x7b can exceed 40GB, causing sustained disk reads that pin your SSD at maximum utilization. This becomes especially problematic when multiple users or applications trigger rapid model switches, creating a cascade of disk I/O that degrades system responsiveness. ...

TL;DR This guide walks you through deploying Stable Diffusion on your own Linux machine using ComfyUI and Automatic1111 (A1111), giving you complete control over your image generation pipeline without sending prompts or outputs to third-party services. You need an NVIDIA GPU with at least 6GB VRAM for basic operation. Cards like the RTX 3060 work well for standard 512x512 images, while RTX 4090 or A6000 cards handle larger resolutions and batch processing. AMD GPUs work through ROCm but require additional configuration. CPU generation is possible but extremely slow. ...

TL;DR LM Studio is a desktop GUI application for running large language models locally on Ubuntu 2026. Unlike command-line tools, it provides a graphical interface for downloading models from Hugging Face and running them without sending data to external servers. The application includes a local OpenAI-compatible API server, making it useful for developers who want to test AI integrations privately. ...

TL;DR The ollama serve command launches the Ollama daemon that exposes a REST API on port 11434 for running local LLM inference. Unlike the simpler ollama run command for interactive chat, serve mode is designed for persistent server deployments where multiple applications need programmatic access to your models. After installing Ollama with curl -fsSL https://ollama.com/install.sh | sh, the service typically starts automatically via systemd on Linux. You can verify it’s running with systemctl status ollama or by checking if port 11434 responds to API requests. The daemon loads models on-demand when applications request them through the HTTP API. ...

TL;DR LM Studio provides a straightforward path to running Gemma 2 models locally on Linux servers, giving you an offline AI assistant for system administration tasks without sending sensitive infrastructure data to external APIs. The CLI interface integrates cleanly with shell scripts, allowing you to pipe system logs, configuration files, and command outputs directly to the model for analysis and recommendations. ...

TL;DR This guide demonstrates building a TypeScript-based web scraper that uses LLMs to parse unstructured server monitoring data from vendor dashboards, legacy admin panels, and third-party SaaS platforms. You’ll integrate OpenAI’s API or local models like Llama 3 to extract metrics, interpret alert messages, and normalize data into Prometheus-compatible formats. ...

TL;DR Retrieval-Augmented Generation systems combine large language models with your actual Linux server documentation, configuration files, and system logs to provide context-aware assistance for file management and system administration tasks. Instead of relying on generic AI responses, RAG systems query your specific infrastructure knowledge base before generating answers, making recommendations directly applicable to your environment. ...

Running llama.cpp Server for Local AI Inference TL;DR llama.cpp server mode transforms the C/C++ inference engine into a production-ready HTTP API server that handles concurrent requests with OpenAI-compatible endpoints. Instead of running single inference sessions, llama-server lets you deploy local LLMs as persistent services that multiple applications can query simultaneously. ...

Linux GPU Hotplug: Optimizing Detection for Ollama TL;DR Linux hardware hotplug events let your system detect and configure GPUs automatically when they appear or change state. For local LLM deployments with Ollama and LM Studio, proper hotplug handling ensures your models can leverage GPU acceleration without manual intervention after driver updates, system reboots, or hardware changes. ...