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Ollama deployment notes

Local model runner used by many self-hosted AI tools.

Deployment verdict

Ollama is infrastructure for local model work, not a complete assistant by itself. Its value is that many self-hosted AI tools can use it as a local model backend. The key evaluation question is hardware reality: model size, memory, speed, and model license matter more than whether the first command succeeds.

Before installing

  • Review the license: MIT.
  • Check whether Docker is supported: yes.
  • Check API key dependency: not required.
  • Confirm supported models: Llama, Qwen, Mistral, DeepSeek.

Recommended deployment path

  1. Install Ollama and pull one small model first.
  2. Run a known prompt set directly against Ollama before adding a UI.
  3. Connect one downstream tool and repeat the same prompts.
  4. Record latency, memory pressure, and answer quality before choosing a larger model.

Common evaluation traps

  • A model that runs is not necessarily good enough for the workflow.
  • Local deployment does not remove license obligations.
  • Bigger models can make the whole stack feel unreliable on weak hardware.

Acceptance test tasks

  1. Run one small model and one larger model on the same prompt set.
  2. Measure response time subjectively and record unusable delays.
  3. Connect one UI and confirm whether answers match direct Ollama behavior.

Setup commands

git clone https://github.com/ollama/ollama.gitRead README and copy the example environment fileStart with Docker if the project provides compose files