Ollama turned running a local LLM from a genuinely fiddly process, downloading weights, picking a quantization, configuring a runtime, into something closer to a single terminal command: ollama run, followed by a model name, and it handles downloading, quantization, and serving automatically. That simplicity is almost entirely why it became the default on-ramp for local LLM usage rather than any specific technical innovation in model serving itself.
It exposes an OpenAI-compatible API locally, which means a huge amount of existing tooling built to talk to OpenAI's API, including some IDE AI plugins and agent frameworks, works against a local Ollama instance with only a URL change, no separate integration required. It supports a wide range of open models, including Llama, DeepSeek, Qwen, and Mistral's open releases, pulling whichever one a user wants from its own model registry.
Being free, open source, and genuinely simple to use, it's become the first thing most developers try when they want to run a model locally for privacy, cost, or offline reasons, with enough adoption that other tools, including Continue, JetBrains AI, and TmuxAI among many others, specifically support Ollama as a local backend option.