Getting Started

TL;DR No, Ollama models cannot access the internet directly. Models running through Ollama are completely offline and operate only on the data they were trained on plus whatever context you provide in your prompts. When you run ollama run llama3.2 or send requests to the API on port 11434, the model generates responses based purely on its training data and your conversation history – it has no mechanism to fetch live web content, query APIs, or retrieve current information. ...

TL;DR llama.cpp remains the fastest way to run quantized LLMs locally in 2026, but choosing the right command-line flags makes the difference between a sluggish 2 tokens/second and a responsive 30+ tokens/second experience. This guide covers the essential flags you need for optimal performance on consumer hardware. The most impactful flags control resource allocation: --n-gpu-layers offloads model layers to your GPU (start with -ngl 35 for 8GB VRAM), --threads sets CPU cores for processing (use physical cores minus 2), and --ctx-size defines context window length (2048 for chat, 8192 for document analysis). Getting these three right solves most performance issues. ...

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 Odysseus transforms your self-hosted infrastructure into a unified AI workspace that coordinates multiple capabilities – chat interfaces, code completion, image generation, and document analysis – through a single web interface. Unlike single-purpose tools that handle one task, Odysseus provides workspace management features designed for teams and complex projects that span multiple AI modalities. ...

TL;DR Running Llama models on AMD GPUs requires ROCm-specific optimizations that differ significantly from NVIDIA CUDA workflows. This guide covers the complete setup for self-hosting Llama 2, Llama 3, and Code Llama variants on AMD hardware using ROCm 6.0+, with focus on memory management, compilation flags, and performance tuning that existing NVIDIA guides do not address. ...

TL;DR DeepSeek v4 runs locally through Ollama with Open WebUI providing a chat interface. This guide covers installation, model-specific configuration for DeepSeek’s extended context window, and performance tuning for the model’s unique reasoning architecture. Install Ollama first, then pull the DeepSeek v4 model: curl -fsSL https://ollama.com/install.sh | sh ollama pull deepseek-v4 DeepSeek v4 requires specific memory allocation due to its 128K token context window. Set OLLAMA_NUM_GPU to control GPU layer offloading – most systems benefit from full GPU utilization with this model’s architecture: ...

TL;DR Open WebUI Desktop brings self-hosted AI to your machine without Docker containers or browser tabs. Download the native application for Windows, macOS, or Linux, and you get a system tray icon, offline-first architecture, and direct file system access – no port mapping or container orchestration required. The desktop version connects to local Ollama instances or OpenAI-compatible APIs just like the web version, but runs as a standalone application with OS-level integration. Launch it from your applications menu, minimize to tray, and interact with models like llama3.2, mistral, or codellama without opening a browser. Updates arrive automatically through the built-in updater, eliminating manual Docker image pulls. ...

TL;DR The OLLAMA_NUM_GPU environment variable controls how many GPUs Ollama uses for inference, but setting it correctly requires understanding your hardware topology and workload patterns. Unlike single-GPU setups where Ollama auto-detects your card, multi-GPU configurations demand explicit tuning to avoid memory fragmentation and PCIe bottlenecks. Set OLLAMA_NUM_GPU=2 to split model layers across two GPUs, or OLLAMA_NUM_GPU=4 for quad-GPU systems. Ollama distributes transformer layers sequentially – GPU 0 handles the first N layers, GPU 1 takes the next batch, and so on. This differs from data parallelism where each GPU processes different prompts simultaneously. ...

TL;DR llama.cpp now handles inverse kinematics calculations through specialized GGUF models that generate joint angles and motion paths for robotic systems. You run llama-server with an IK-trained model, send it target positions as JSON prompts, and receive executable motion commands. This works entirely offline without cloud dependencies. The typical workflow involves loading a quantized IK model (Q4_K_M or Q5_K_M recommended for speed), sending coordinate targets through the OpenAI-compatible HTTP API, and parsing the structured output into robot control commands. Models like CodeLlama-IK and specialized Llama variants trained on robotics datasets handle 6-DOF arm calculations, path planning with obstacle avoidance, and real-time trajectory adjustments. ...

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. ...