Local AI Engineering with Ollama

What It Actually Solves

Before Ollama, running a model locally meant assembling the stack yourself:

• Compiled an inference engine from source (like llama.cpp)
• Hunted for model files (like GGUF files) on registries and repositories (like Hugging Face)
• Figured out how many layers your GPU could hold
• Wrote a wrapper to keep the model loaded between requests
• Then wrote a second wrapper because your app expected an OpenAI-shaped API
• And then a third wrapper to manage chat history and system prompts
• And probably more things

Every new model meant repeating most of those steps. None of it was hard in the research-paper sense. It was just tedious, error-prone glue work that nobody wanted to do twice.

Ollama collapses that work into 3 observable behaviors: