How to Run a Local LLM in 2026

A year ago, "run an LLM locally" mostly meant a hobby project — you'd get a small model chatting on your laptop, be mildly impressed, and go back to using ChatGPT for anything that actually mattered. That gap has closed a lot faster than I expected. The open models available today are genuinely usable for real work, not just for showing off that it runs offline.

I get asked some version of this question a lot, usually from people who work with data they can't upload anywhere: can I just run the AI myself? The short answer is yes. The longer answer is that "running a local LLM" is actually two separate decisions that get mashed together in most guides, and untangling them makes the whole thing much less confusing.

It's two decisions, not one

The model is the neural network — Qwen, Gemma, Llama, DeepSeek, and so on. Models differ in coding ability, reasoning, context length, speed, and license.

The runtime is the software that downloads the model and serves it to you or to another application — Ollama, LM Studio, Jan, vLLM. Runtimes differ in interface (terminal vs. GUI), platform support, and whether they're built for one person or a whole team.

You pick one of each. A developer connecting a local model to another tool typically ends up with Ollama running a Qwen or Gemma model. Someone who just wants a private chat window on their laptop typically ends up with LM Studio running whatever fits their machine. Neither is wrong — they're solving slightly different problems.

Why bother running a model locally

The honest reason is where inference happens. With a local model, your prompt, your data, and the output never leave your computer. With a cloud model, they're sent to someone else's server, subject to someone else's retention policy.

That distinction stops being abstract fast once you're working with:

confidential company data or unreleased product plans
financial records
patient or healthcare data
customer data under GDPR, HIPAA, or similar rules
anything you're contractually barred from uploading to a third party

There's a cost angle too — no per-token bill, no rate limits — and an availability angle: it keeps working with no internet connection. None of that means local is automatically better. A small model on a laptop will lose to a frontier cloud model on a genuinely hard reasoning problem. It just means you get to choose, project by project, instead of being locked into one provider by default.

Your runtime options

Here's how the main ones compare right now:

Runtime	Best for	Interface	Notes
Ollama	Developers, automation, connecting to other apps	Terminal, OpenAI-compatible API	The closest thing to a default. One command pulls a model and serves an API that other software can call directly.
LM Studio	Non-technical users, Apple Silicon	GUI	Model browser, chat window, one-click downloads. Free for commercial use, and strong on Mac hardware.
Jan	Privacy purists	GUI, fully open source	Offline by default, no telemetry, auditable code. Smaller model library than LM Studio. Tool-calling support is still catching up to the others.
vLLM	Serving a model to a team	Server / API	Built for concurrent, multi-user throughput, not a single laptop. The right tool once "local" means "our infrastructure" rather than "my machine."
llama.cpp	Embedded or custom setups	Terminal / library	The lower-level engine several of the above are built on top of. More control, more setup.

For most single-user work, Ollama is the least friction — it installs in minutes and exposes a standard API that other desktop AI tools are usually built to talk to.

Which model should you run

This comes down to your hardware more than chasing a leaderboard. As a rough starting point by memory tier:

8 GB RAM, no dedicated GPU — a small model in the 1–4B range
16 GB RAM / 8–12 GB VRAM — a mid-size model in the 7–14B range
24 GB VRAM — a strong 24–32B coding/reasoning model
48 GB+ VRAM, or Apple Silicon with a lot of unified memory — a larger model, or more context

On families: Qwen and Gemma currently have some of the strongest general coding and instruction-following behavior at small-to-mid sizes, which is why they're the default "try this first" pick in most local-LLM guides — and, for what it's worth, why they're the example models in MLJAR Studio's own Ollama setup docs. DeepSeek tends to lead on harder reasoning and math-heavy tasks. Llama remains the most broadly supported across every runtime, so it's a reasonable fallback if a newer model gives you trouble.

Model rankings shift every few months. Treat any specific benchmark number you read as a snapshot, not a permanent fact. Pick something that fits your hardware tier, run it against your actual work, and swap it if it struggles — that's a five-minute decision, not a research project.

Getting started in four steps

Install a runtime. For most people, that's Ollama — download it, install it, done.
Pull a model. ollama pull qwen-3.5:27b (or any model name from Ollama's library) downloads the weights to your machine.
Test it. ollama run qwen-3.5:27b opens a local chat session. No internet required from here on.
Connect it to your workflow. Ollama serves an OpenAI-compatible API on localhost:11434 by default — that's what lets other applications use your local model instead of a cloud one.

What can you actually do with a local LLM?

This is where most guides stop at "you can chat with it," which undersells the point. Once a model is running locally with an API behind it, it's not a novelty chat window — it's a component you can plug into real work.

Coding help. Local models are genuinely good at this now — autocomplete, refactoring, explaining unfamiliar code, writing tests. Pair Ollama with an editor extension (Continue.dev is a common choice) and you get most of what a cloud coding assistant gives you, without your codebase leaving your machine. This matters a lot for proprietary or client code under NDA.

Writing, chat, and summarization. Drafting, editing, brainstorming, condensing a long document into a few bullet points — the everyday assistant tasks work fine locally, and for anything containing information you'd rather not send to a third party, it's the obvious default.

Talking to your own documents, privately. Point a local model at a folder of PDFs, notes, or internal wikis (typically via a lightweight retrieval setup on top of the model) and ask questions across all of it. This is one of the more underrated use cases — a private, searchable "chat with my files" that never touches the internet.

Data analysis. This is the one I spend the most time on, so it's worth being precise about how it actually works: a local LLM doesn't calculate anything itself. It reads your question, writes Python, and Python — not the model — does the real computation against your data. That's the same pattern cloud AI data-analysis tools use; running local AI data analysis just means the model, the code, and the data all stay on your machine. MLJAR Studio, the desktop app I build, documents exactly this setup through Ollama — connect it once in provider settings, and your notebook, prompts, and data never leave your computer from that point on.

Light automation and agents. Local models can call tools and trigger multi-step workflows, but support varies more here than in chat — Ollama and LM Studio expose OpenAI-compatible function calling, Jan's support is still maturing. If you're building anything agentic, check your runtime's tool-calling story before you commit to it.

Working offline, or somewhere you shouldn't be uploading anything. Field research, secure facilities, air-gapped environments, a flight with bad wifi — a local model is the only option that just keeps working, because there's nothing to connect to in the first place.

Local doesn't mean giving up the cloud

You don't have to commit to one approach forever. Most practical setups mix both, depending on the task:

Sensitive or regulated data → local model
Everyday, low-stakes work → either
A genuinely hard reasoning problem → a strong cloud model
No internet / air-gapped → local, no other option

The point isn't that local is always the right call — it's having that choice available per project instead of being locked into one provider for everything.

A few questions I get asked a lot (FAQ)

Do I need a GPU?

No. It makes things faster, but a small quantized model runs acceptably on CPU with enough RAM.

Is Ollama the only way to do this?

No — LM Studio, Jan, and vLLM are all legitimate choices depending on whether you want a GUI, maximum privacy, or team-scale serving. Ollama is just the most common default, and the one most third-party tools integrate with first.

Will a local model match GPT or Claude?

For a lot of everyday work, close enough that it doesn't matter. For the hardest problems, cloud frontier models still tend to have an edge — which is exactly why most people end up with a mixed setup rather than an all-or-nothing choice.

Does local automatically mean private?

Only if inference is actually happening on your machine. Some "local-sounding" services quietly run on remote infrastructure — worth checking before you assume a setup is private.

If you want to see this pattern in practice rather than in theory — a local model writing Python, Python doing the actual analysis, everything staying on your machine — that's what MLJAR Studio is built around.