Posts

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 Finetuning your local LLM on copyrighted material creates the same legal risks as training foundation models, but with direct personal liability. When you run ollama create mymodel -f Modelfile using a dataset scraped from Stack Overflow, GitHub repositories, or published books, you become the party responsible for any copyright infringement – not a distant corporation with legal teams. ...

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 Deploying Ollama on Kubernetes transforms local AI inference into a production-grade service with horizontal scaling, persistent model storage, and service mesh integration. This guide covers container orchestration patterns specifically for LLM workloads running on self-hosted infrastructure. The core deployment uses StatefulSets rather than Deployments to maintain stable network identities and persistent volume claims for model storage. Each Ollama pod serves the REST API on port 11434 and requires GPU node affinity when using NVIDIA runtime. Configure OLLAMA_HOST=0.0.0.0:11434 to bind the service to all interfaces within the pod network, and set OLLAMA_MODELS=/models pointing to your PersistentVolume mount path. ...

TL;DR Ollama Cloud offers managed hosting with zero infrastructure overhead, while local self-hosting gives you complete control and predictable costs after initial hardware investment. The decision hinges on your request volume, data sensitivity requirements, and whether you already own suitable hardware. For teams processing fewer than several thousand requests daily, Ollama Cloud eliminates the need to manage GPU servers, handle model updates, or troubleshoot CUDA driver conflicts. You pay per API call without worrying about idle capacity. Local hosting becomes cost-effective when you have consistent high-volume workloads that would generate substantial API bills – think continuous document processing pipelines or customer service chatbots handling hundreds of concurrent sessions. ...

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 This guide walks you through deploying a complete local LLM stack on Mac Mini hardware, specifically optimized for Apple Silicon’s unified memory architecture. You’ll install Ollama as your model runtime and Open WebUI as your chat interface, creating a private AI environment that keeps all data on your local network. The Mac Mini M2 Pro and M4 models excel at running 7B to 13B parameter models thanks to their high-bandwidth unified memory. Unlike traditional GPU setups, Apple Silicon shares memory between CPU and GPU cores, eliminating PCIe bottlenecks. This architecture means a Mac Mini with 32GB RAM can comfortably run llama3.1:8b or mistral:7b models while leaving headroom for the web interface and system processes. ...

TL;DR Running Qwen 3.5 locally requires choosing between Ollama’s CLI-first approach and LM Studio’s GUI-driven workflow. Both tools serve the same GGUF model files but differ significantly in performance characteristics and operational overhead. Ollama excels at automated deployments and scripting. Install with curl -fsSL https://ollama.com/install.sh | sh, pull the model using ollama pull qwen2.5-coder:7b, and start serving on port 11434. Memory usage stays consistent across inference requests, making it predictable for containerized environments. The CLI interface integrates cleanly with shell scripts and CI/CD pipelines. ...