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Ollama vs LM Studio vs llama.cpp (2026): Best Way to Run LLMs Locally

Our verdict

Start with Ollama if you want local models behind an API for your apps and coding agents: it's MIT-licensed and a one-line install on macOS, Windows and Linux. Pick LM Studio if you'd rather work in a desktop app, especially on an Apple Silicon Mac, where it also runs MLX models. Use llama.cpp directly when you want the engine itself, with the most control over hardware backends and quantization.

Ollama

Free / cloud from $20/mo

Run open models such as Gemma, Qwen, DeepSeek and gpt-oss on your own computer with one command, then use them through a local REST API, Python or JavaScript, or coding agents like Claude Code and Codex. Optional cloud models cover anything too big for your hardware.

Read our review Official site ↗

LM Studio

Free

Desktop app for macOS, Windows and Linux that finds, downloads and runs open models locally (GGUF through llama.cpp, MLX on Apple Silicon), with chat, offline document chat, MCP tools and an OpenAI-compatible local server.

Read our review Official site ↗

llama.cpp

Open Source

The open-source C/C++ engine for running LLMs and vision models locally, with a CLI, an OpenAI-compatible server with a built-in web UI, 1.5-bit to 8-bit quantization, and backends from CUDA and Metal to Vulkan and SYCL.

Read our review Official site ↗

Side by side

What it is
Ollama: Local model runner with a CLI, an app and a REST API, plus optional cloud models
LM Studio: Desktop app for finding, chatting with and serving local models
llama.cpp: The C/C++ inference engine, with a CLI and a server
License
Ollama: MIT
LM Studio: Closed-source app, free for home and work; CLI, SDKs and MLX engine are MIT
llama.cpp: MIT
GitHub stars (1 Nov 2026)
Ollama: 185.5k
LM Studio: App not on GitHub; lms CLI 5.4k
llama.cpp: 133k
How you use it
Ollama: Terminal (ollama run) or the app
LM Studio: Desktop GUI, the lms CLI, or headless with llmster
llama.cpp: Terminal (llama cli), or the web UI in llama serve
API for your apps
Ollama: REST API on localhost:11434; works with OpenAI and Anthropic clients
LM Studio: OpenAI-compatible endpoints, plus a beta LM Studio REST API
llama.cpp: OpenAI-compatible server (llama serve)
Getting models
Ollama: Ollama's model library at ollama.com/search
LM Studio: Hugging Face search inside the app; GGUF, plus MLX on Apple Silicon
llama.cpp: GGUF files, downloaded from Hugging Face with -hf
Platforms
Ollama: macOS, Windows, Linux, Docker
LM Studio: Apple Silicon Macs, x64 and ARM64 Windows, x64 Linux
llama.cpp: macOS, Windows, Linux, Docker; CPU plus CUDA, HIP, Metal, Vulkan, SYCL and more
Coding agents
Ollama: ollama launch starts Claude Code, Codex CLI or OpenCode
LM Studio: Integration guides for tools such as Claude Code
llama.cpp: Point any OpenAI-compatible tool at llama serve
Cost
Ollama: Free locally; cloud plans: Pro $20/mo, Max $100/mo, Team $500/mo
LM Studio: Free; Teams and Enterprise plans on request
llama.cpp: Free

Which should you choose?

Choose Ollama if…

  • You want a local model behind an API in one command.
  • You want to try coding agents like Claude Code or Codex on open models.
  • You'd like to fall back to cloud models when a model won't fit on your machine.

Choose LM Studio if…

  • You'd rather browse, download and chat with models in an app than in a terminal.
  • You're on an Apple Silicon Mac and want MLX models as well as GGUF.
  • You want offline document chat and MCP tools without writing code.

Choose llama.cpp if…

  • You want the engine directly, with no app layer on top.
  • You need a specific backend, such as Vulkan, SYCL for Intel GPUs, or Ascend NPUs.
  • You want to choose your own quantization, from 1.5-bit to 8-bit.

Three layers of the same idea

All three run open models on your own hardware, but they sit at different layers.

  • llama.cpp is the engine: a C/C++ library and tools that do the actual inference. LM Studio uses it for GGUF models.
  • Ollama is a runner: it manages models for you and puts them behind a local API that apps and coding agents can call.
  • LM Studio is a desktop app: it helps you find models, chat with them and serve them, with a GUI on top of llama.cpp and Apple’s MLX.

Getting started

Ollama takes one command once it’s installed:

ollama run gemma4:e2b

LM Studio is a download from lmstudio.ai. Search for a model, download it and start chatting, then turn on the local server when an app needs it.

llama.cpp installs with a script and can pull a GGUF model straight from Hugging Face:

curl -LsSf https://llama.app/install.sh | sh
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

Using them from your code

All three give you an OpenAI-compatible endpoint, so most tools that speak the OpenAI API can switch to a local model by changing the base URL. Ollama also works with Anthropic clients and has its own Python and JavaScript libraries. LM Studio adds Python and TypeScript SDKs. llama.cpp gives you the server and leaves the rest to you.

Privacy and the cloud

llama.cpp runs entirely on your machine, and so does LM Studio’s model runtime. Ollama is local by default too, but it also offers cloud models for anything your hardware can’t handle. Ollama says it doesn’t use cloud prompts to train models, and you can turn its cloud features off if everything must stay on your hardware.

The bottom line

  • Building an app, or running a coding agent on local models: Ollama.
  • Exploring models in a desktop app, or running MLX on a Mac: LM Studio.
  • Maximum control, unusual hardware, or embedding inference yourself: llama.cpp.

Building on top of a local model? Read LangChain vs LangGraph.

How we compared

Facts come from each project’s official GitHub repository, documentation and pricing pages, checked on 1 November 2026 and listed below. We haven’t benchmarked the three tools, so nothing here is a speed claim. Check the official pages before committing, and tell us if something has changed.

Frequently asked questions

What are the best Ollama alternatives?

LM Studio and llama.cpp are the two closest. LM Studio is a desktop app with model search, chat and an OpenAI-compatible local server, and it's free for work use. llama.cpp is the MIT-licensed C/C++ engine with its own CLI and OpenAI-compatible server. Pick LM Studio for a GUI and llama.cpp for maximum control.

Is Ollama free?

Yes for local use: running models on your own hardware is free and unlimited. Ollama's paid plans are for its cloud models. Pro is $20 a month or $200 a year with $60 of usage credits a month, Max is $100 a month with $300 of credits, and Team (early access) is $500 a month.

Is LM Studio free for commercial use?

Yes. Since 8 July 2025, LM Studio's terms let companies use it at work without a separate license or form. Teams and Enterprise plans add private sharing, SSO and model and MCP gating; their prices aren't published.

Is LM Studio open source?

No. The desktop app is closed source, though free to use. Its lms command-line tool, Python and TypeScript SDKs and MLX engine are MIT-licensed on GitHub.

Does LM Studio use llama.cpp?

Yes. LM Studio runs GGUF models through llama.cpp on macOS, Windows and Linux, and MLX models on Apple Silicon Macs.

Which one is fastest?

We haven't benchmarked them, so we won't name a winner. Try the model you actually plan to use, at the quantization you plan to use, on your own hardware in each tool before you decide.

What does Ollama Pro cost in the UAE?

Ollama lists its prices in US dollars. At the fixed rate of 3.6725 AED per dollar, $20 a month is about AED 73, and the $200 annual plan is about AED 735, before any VAT.

Sources

Published November 1, 2026. Spotted something out of date? Let us know.

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