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Articles, guides, and insights on local artificial intelligence
Reading paths
Start here
The in-order path for newcomers to local AI: from basic concepts to installation and your first model.
Optimize performance
Pick the right hardware and get the most out of what you already own: CPU vs GPU, Mac, Raspberry Pi, and fast serving.
Data and privacy
What actually goes over the network with local AI: the data, the security, the compliance, and the control that stays in your hands.
Choosing hardware and model
How to pick the right hardware and the model suited to your use: VRAM, quantization, GPU or CPU and response speed.
7 articles
Local voice: from Whisper to text-to-speech
How to build a fully local voice pipeline: transcription with Whisper, synthesis with Piper or Kokoro-82M, hardware requirements, and practical use cases.
Fine-tuning: adapting a model to your data with LoRA and QLoRA
Fine-tuning with LoRA and QLoRA: when it's truly needed, how to prepare 100-500 quality examples, how much VRAM it takes (8-12 GB in QLoRA for a 7B), and which local tools to use: LLaMA-Factory, Unsloth, MLX.
Open WebUI: the interface for your local AI
Open WebUI is the full web interface for your local models: Docker install, connecting Ollama, per-model customization, document RAG, and its honest limits.
Local AI on Mac: the complete Apple Silicon guide
Why Apple Silicon (unified memory, GPU cores) is the simplest machine for local AI: the RAM you need, installing Ollama, recommended models per chip generation, and realistic performance expectations.
Running LLM Models Locally: From First Install to First Working Model
Learn how to install and use large language models on your own computer, without relying on cloud services.
Local AI on Raspberry Pi
From first install to a model that answers on a Pi 5: real 2026 prices, the tok/s = bandwidth ÷ file formula, the electricity bill and the tricks that matter.
How to Choose Your First Local AI Model
The three criteria for picking your first local model: use case, memory (with the exact formula) and context, with the recommended models for 8, 16 and 24 GB.