Local AI refers to running AI models on customer hardware (laptops, workstations, on-premise servers, edge devices) rather than via cloud APIs. The pattern eliminates per-request subscription costs, keeps data fully on customer infrastructure, supports air-gapped and regulated environments, and enables customization that cloud APIs don’t permit. Articles cover the local AI landscape, the runtime tools, the open-weight models that run locally, and the operational guides for teams adopting local inference.
Ollama is the open-source command-line and HTTP-API tool that became the most-adopted local LLM runtime through 2024 and 2026 by abstracting away the substantial complexity of model loading, quantization, and serving into a few simple commands. The...
Local AI models are large language models that run on customer hardware (laptops, workstations, on-premise servers, or edge devices) rather than via cloud APIs. The pattern has matured substantially through 2024-2026 to the point where running a...
