How to Run LLMs Locally with Ollama: Setup, Models, and Best Practices

Running large language models (LLMs) locally has gone from “fun weekend experiment” to a genuinely practical setup for developers, makers, and teams who want more privacy, lower marginal costs, and tighter control over latency. Ollama is one of the most popular ways to do this because it wraps a lot of the fiddly parts into a workflow that feels closer to “install → run → build.”

Below is a practical guide to getting Ollama running, picking the right models, and avoiding the common gotchas that make local LLMs feel slower (or more confusing) than they need to be.

Why run LLMs locally in the first place?

Local inference is a strong fit when you care about any of the following:

• Privacy and data control: prompts and responses don’t have to leave your machine (or your own server).
• Predictable costs: no per-token bill; your “cost” is hardware and electricity.
• Low latency for local apps: especially when your app and model are on the same box or LAN.
• Offline / restricted environments: useful for dev environments, regulated workflows, or spotty internet.

The trade-off is that you’re now responsible for hardware sizing, updates, and performance tuning, but with the right setup, it’s very manageable.

Installing Ollama (quick setup)

Ollama supports common operating systems and is typically installed via a desktop installer (Windows/macOS) or a script/service on Linux. Once installed, you’ll generally interact with it through the CLI and/or its local HTTP API. Curious to explore more into this? Check out this detailed blog on “What is Ollama and how to use it”.

However, a typical first run looks like:

• Install Ollama
• Pull a model
• Start chatting/calling the API

Getting your first model running

After installation, the basic flow is:

1. Download (pull) a model
2. Run it interactively or call it via API

You’ll see model names like Llama, Qwen, Mistral, Gemma, Phi, and others. In practice, you’ll choose based on your hardware and your use case (coding, summarisation, Q&A, instruction following, etc.).

If you’re brand new to local models, start with something lightweight, confirm it runs smoothly, then step up in size.

Choosing models that match your hardware

Model choice is where most people accidentally kneecap performance. A practical rule of thumb to remember is that smaller models feel “snappier.” If you’re on a laptop or modest desktop, start small and only scale up if you truly need it. Another important aspect to bear in mind is that quantised models are your friend. Quantisation reduces memory use and can improve speed (with some quality trade-offs). For local workflows, it’s often the difference between “usable daily” and “painful.”

Also note that long context windows can be tempting, but they increase memory pressure and can slow generation if you’re constantly feeding huge prompts.

Serving Ollama like a local “LLM backend”

One of Ollama’s biggest wins is that it can behave like a local service that your apps can call. That enables:

• Local chat UIs
• Internal tools
• Scripts that summarise documents
• Automation workflows (like n8n)
• RAG pipelines (retrieval-augmented generation)

If you’re building anything beyond personal tinkering, think of Ollama as an internal dependency: treat it like a service you monitor, update, and secure.

Best practices for performance and reliability

Here’s what tends to make the biggest difference in day-to-day use.

• Keep prompts tight and structured

Local models don’t magically become better because you sent them a novel. If your prompts are long and messy, you’ll pay for it in latency and hallucination risk. Instead, you need clear instructions, explicit output format, and only the context that’s actually needed.

• Use “system” instructions consistently

If you have repeated behavioural requirements (tone, output schema, constraints), put them in a consistent system prompt (or an equivalent pattern in your app). This helps the model behave more predictably without repeatedly bloating user prompts.

• Treat temperature and sampling as knobs, not vibes

For factual tasks, lower creativity is usually better. For brainstorming, raise it. If you’re building an app for others, consider locking these settings down per task so outputs are repeatable.

• Separate “dev” from “always-on”

If you want Ollama to be stable for automations, run it in a more server-like way (especially on Linux). For experimentation, a laptop setup is fine; for reliability, a small dedicated box (or VPS with suitable resources) is often cleaner.

Security and privacy basics (don’t skip these)

Running locally is a privacy upgrade, but only if you don’t accidentally expose the service. To avoid this, you need bind to localhost unless you truly need LAN access. If you do expose it on a network, add authentication (or put it behind a reverse proxy with auth).

Final takeaway

Ollama makes local LLMs approachable, but the real magic comes from choosing the right model size, keeping prompts lean, and treating your local setup like a real service, especially if it’s powering workflows or tools other people rely on. Take your time researching this before you get going, and you’ll find it benefits you in the long run.

(Disclaimer: Devdiscourse's journalists were not involved in the production of this article. The facts and opinions appearing in the article do not reflect the views of Devdiscourse and Devdiscourse does not claim any responsibility for the same.)