Run AI Locally. Own Your Data.

TL;DR Both Ollama and llama.cpp let you control how your local LLMs behave through runtime parameters. Understanding these settings helps you balance response quality, speed, and resource usage without sending data to external APIs. Temperature controls randomness – lower values like 0.1 produce focused, deterministic outputs while higher values like 0.9 generate creative but less predictable text. Top-p (nucleus sampling) filters token choices by cumulative probability, typically set between 0.7 and 0.95. Context window size determines how much conversation history the model remembers, ranging from 2048 to 128000 tokens depending on your model and available VRAM. ...

TL;DR Hugging Face serves as the primary model repository for self-hosted AI deployments, but navigating its ecosystem requires specific skills beyond basic model downloads. You need to understand model cards, quantization formats, and licensing before pulling multi-gigabyte files into your homelab. Start by learning to read model cards on Hugging Face – they contain critical information about context windows, training data, and recommended inference parameters. For Ollama deployments, look for GGUF format models or Modelfiles that reference Hugging Face repositories. LM Studio users should focus on models with clear quantization levels (Q4_K_M, Q5_K_S) that balance quality and VRAM usage. ...

TL;DR Building llama.cpp from source gives you a high-performance C/C++ inference engine for running GGUF-format language models locally without cloud dependencies. The process involves cloning the GitHub repository, installing build dependencies like cmake and a C++ compiler, then compiling with hardware acceleration flags for your CPU or GPU. The main advantage of building from source rather than using pre-built binaries is control over optimization flags and hardware support. You can enable CUDA for NVIDIA GPUs, ROCm for AMD cards, or Metal for Apple Silicon. CPU-only builds work everywhere but run slower on large models. ...

TL;DR OpenClaw Framework provides a structured approach to building AI-powered command-line tools that integrate with local LLMs running in LM Studio. Instead of sending your terminal commands and system data to cloud APIs, OpenClaw routes everything through your local inference server, keeping sensitive information on your machine. The framework handles the connection between your shell environment and LM Studio’s OpenAI-compatible API server, which runs on port 1234 by default. You write Python scripts that describe what you want the AI to do – generate shell commands, analyze log files, suggest configuration changes – and OpenClaw manages the prompt formatting, context injection, and response parsing. ...

TL;DR Ollama is a command-line tool that lets you run large language models like Llama, Mistral, and CodeLlama directly on your Linux machine without sending data to external APIs. Install it with a single command, pull models from the ollama.com library, and interact via REST API on port 11434 or through the CLI. ...

TL;DR You can run Claude-quality coding models on your own hardware using Ollama and Open WebUI, keeping your code and conversations completely private. This guide walks you through deploying models like DeepSeek Coder, Qwen2.5-Coder, and CodeLlama that rival proprietary services for code generation, debugging, and refactoring tasks. The setup requires a Linux machine with at least 16GB RAM for 7B models or 32GB+ for 34B models. You’ll install Ollama as the model runtime, pull coding-focused models, then connect Open WebUI as your chat interface. The entire stack runs locally—no API keys, no data leaving your network. ...

TL;DR Generic AI chatbots give generic answers. Fine-tuned AI models sound like your business, know your products, and follow your rules. For small businesses, this means 24/7 customer support that actually represents your company accurately. The business case: Cost to fine-tune: Varies by model size and provider – expect a modest one-time investment Monthly hosting: Depends on hardware or cloud choice What it replaces: Hours of daily repetitive customer inquiries Typical ROI: Many businesses recoup costs within a few months Who it works for: Any business that answers the same types of questions repeatedly — service companies, professional firms, retail, healthcare, real estate. ...

TL;DR The NVIDIA RTX 3090 is the best price-to-performance GPU for local AI work in 2026. At $700-900 used, it delivers 24GB of VRAM — the same amount as GPUs costing 2-3x more. That 24GB is the critical spec: it determines which models you can run and how many customers you can serve. ...

TL;DR You can run your own OpenAI-compatible API on a single machine with a GPU. Your data never leaves your hardware, costs are fixed instead of per-token, and you can serve custom fine-tuned models. What you get: A drop-in replacement for the OpenAI API (change one line of code to switch) Complete data privacy — nothing sent to external servers Fixed monthly cost instead of unpredictable per-token billing Custom models fine-tuned on your business data No per-seat licensing Minimum setup: ...

TL;DR Fine-tuning takes a general-purpose AI model like Llama 3 and trains it further on your business data. The result is a model that responds in your company’s voice, knows your products, and follows your rules — not a generic chatbot. What you need: 200-500 question/answer pairs from your business A GPU with 24GB VRAM (RTX 3090, ~$800 used) or a MacBook with 32GB 2-6 hours of training time QLoRA + Hugging Face tools (all free and open source) What you get: ...