Ollama AI Guides

This is a single consolidated guide for running local models with Ollama and connecting tools and agents (especially Goose) to your local Ollama server. It merges the core Desktop, CLI, and Goose workflows into one practical "happy path + operations" reference.

plebdevs.com

March 06, 2026 · 6 min read

Ollama Local Hosting — CLI + Desktop Guide


Index#


Overview#

When to use this combined guide.

Mental model.


Setup#

1) Install Ollama#

ollama --version

2) Choose how you run it#

Pick one of these normal local patterns:

For direct terminal hosting:

ollama serve

That starts the local API on http://localhost:11434.

3) Pull and run a starter model#

ollama pull qwen3.5:3b
ollama run qwen3.5:3b

4) Verify the local API#

Check the server itself:

curl http://localhost:11434/api/version

Check inference:

curl http://localhost:11434/api/generate \
  -H 'Content-Type: application/json' \
  -d '{"model":"qwen3.5:3b","prompt":"Say hello from local Ollama","stream":false}'

If you use tools that expect OpenAI-compatible APIs, Ollama also exposes:

curl http://localhost:11434/v1/models

Beginner usage#

  1. Start Ollama with either the desktop app or ollama serve.
  2. Pull a model: ollama pull qwen3.5:3b
  3. Sanity-check it locally with ollama run qwen3.5:3b
  4. Verify the API with curl http://localhost:11434/api/version
  5. Point your agent or tool at http://localhost:11434 or http://localhost:11434/v1
  6. Use the exact local model name, for example qwen3.5:3b

Fast path

ollama serve
ollama pull qwen3.5:3b
ollama run qwen3.5:3b

Pro usage#

Daily desktop workflow#

This is the simplest setup if you want Ollama always available but still want terminal control.

Daily CLI workflow#

Use the terminal when you want explicit control over everything:

ollama serve
ollama pull qwen3.5:3b
ollama run qwen3.5:3b
ollama ps

This is the better fit for headless machines, scripted workflows, and debugging.

Model lifecycle#

ollama ls
ollama ps
ollama pull <model>
ollama show <model>
ollama stop <model>
ollama rm <model[:tag]>

Typical pattern:

Custom models with Modelfile#

Use a Modelfile when you want a reusable local model preset:

FROM qwen3.5:3b
PARAMETER temperature 0.1
SYSTEM You are a terse local coding assistant.

Build and run it:

ollama create my-local-coder -f Modelfile
ollama run my-local-coder

OpenAI-compatible clients#

Many local tools can connect through Ollama's OpenAI-compatible endpoint:

Example:

export OPENAI_BASE_URL=http://localhost:11434/v1/
export OPENAI_API_KEY=ollama

Background service patterns#

Linux example:

sudo systemctl enable ollama
sudo systemctl start ollama
journalctl -u ollama -f

Docker hosting#

Use Docker when you want isolation or a predictable local deployment:

docker run -d --name ollama -p 11434:11434 -v ollama:/root/.ollama ollama/ollama
docker exec -it ollama ollama run qwen3.5:3b

LAN access use sparingly#

The safe default is local-only on 127.0.0.1:11434.

If you intentionally need LAN access:

OLLAMA_HOST=0.0.0.0:11434 ollama serve

Only do this on trusted networks, and preferably behind a proxy with auth.


Cost savings guide#


Privacy guide#

For direct terminal hosting:

ollama serve

That starts the local API on http://localhost:11434.

3) Pull and run a starter model#

ollama pull qwen3.5:3b
ollama run qwen3.5:3b

4) Verify the local API#

Check the server itself:

curl http://localhost:11434/api/version

Check inference:

curl http://localhost:11434/api/generate \
  -H 'Content-Type: application/json' \
  -d '{"model":"qwen3.5:3b","prompt":"Say hello from local Ollama","stream":false}'

If you use tools that expect OpenAI-compatible APIs, Ollama also exposes:

curl http://localhost:11434/v1/models

Beginner usage#

  1. Start Ollama with either the desktop app or ollama serve.
  2. Pull a model: ollama pull qwen3.5:3b
  3. Sanity-check it locally with ollama run qwen3.5:3b
  4. Verify the API with curl http://localhost:11434/api/version
  5. Point your agent or tool at http://localhost:11434 or http://localhost:11434/v1
  6. Use the exact local model name, for example qwen3.5:3b

Fast path

ollama serve
ollama pull qwen3.5:3b
ollama run qwen3.5:3b

Pro usage#

Daily desktop workflow#

#ai#document#guide#ollama
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