🖥️ Running Local & Open-Source LLMs

• Why Run a Model Locally? (6 min) — When you send a prompt to a cloud LLM (large language model — an AI system whose knowledge is encoded in billions of nu…
• Picking an Open Model: Llama, Mistral, Qwen, and Friends (7 min) — ⚡ Open-source LLMs come in families with multiple size variants, measured in billions of parameters (B). Parameter coun…
• Getting Started with Ollama: Your First Local Model in Minutes (7 min) — Ollama is an open-source runtime that packages model weights, a quantization layer, and a local HTTP server into a sing…
• Quantization: Fitting Big Models on Small Hardware (7 min) — Model weights are stored as floating-point numbers. Full precision (FP32) uses 32 bits per parameter; half precision (F…
• Local vs. Cloud: Choosing the Right Deployment (7 min) — Choosing between a local LLM and a cloud API is a deployment decision with real cost, capability, and compliance conseq…
• Building a Private End-to-End Local AI Workflow (8 min) — A fully local AI workflow chains four components on your own hardware: a model server, an embedding engine, a vector st…

Why Run a Model Locally?

When you send a prompt to a cloud LLM (large language model — an AI system whose knowledge is encoded in billions of numerical parameters called weights), that text travels to a remote server, gets processed there, and the response comes back. Running a model locally means the w…

Picking an Open Model: Llama, Mistral, Qwen, and Friends

⚡ Open-source LLMs come in families with multiple size variants, measured in billions of parameters (B). Parameter count is the primary predictor of capability — and of hardware cost. The main families as of mid-2026: **Llama 3.x** (Meta) spans 1B–405B; the 8B and 70B checkpoint…

Getting Started with Ollama: Your First Local Model in Minutes

Ollama is an open-source runtime that packages model weights, a quantization layer, and a local HTTP server into a single installable tool for macOS, Linux, and Windows. After a one-command install (`curl -fsSL https://ollama.com/install.sh | sh` on Linux, or the desktop install…

Quantization: Fitting Big Models on Small Hardware

Model weights are stored as floating-point numbers. Full precision (FP32) uses 32 bits per parameter; half precision (FP16/BF16, meaning 16-bit floats) uses 16 bits. Quantization compresses weights further — to INT8 (8 bits) or INT4 (4 bits) — cutting memory by 4–8× with minimal…

Local vs. Cloud: Choosing the Right Deployment

Choosing between a local LLM and a cloud API is a deployment decision with real cost, capability, and compliance consequences. Local deployment wins in four situations: (1) Data sovereignty — regulated data such as PHI under HIPAA, personal data under GDPR, or documents under le…

Building a Private End-to-End Local AI Workflow

A fully local AI workflow chains four components on your own hardware: a model server, an embedding engine, a vector store, and an orchestration layer. The model server — typically Ollama or llama.cpp — exposes a local OpenAI-compatible REST endpoint at `http://localhost:11434/v…