Start a stopwatch. Pull the same quantized model into Ollama, LM Studio, and Jan, ask each the same question on the same laptop, and the wall-clock times land close together. This surprises people who expect a "fastest local LLM tool" leaderboard. There mostly isn't one, and the reason is that under the branding, all three are running the same engine.
The shared engine, and why it flattens the speed question
Georgi Gerganov's ggml-org/llama.cpp and its GGUF model format are the foundation the local-LLM world is built on. LM Studio runs llama.cpp directly. Jan runs llama.cpp (historically through its Cortex layer, now folding it in more directly). Ollama is the interesting asterisk: it started on llama.cpp and still ships a llama.cpp runner, but since 2025 it also has its own engine for first-class multimodal support. So "built on llama.cpp" is now only partly true for Ollama — and fully true for the other two.
The practical upshot: for one GGUF model on one machine, single-stream throughput is broadly similar across all three. The one engine-level lever that actually moves the number is Apple's MLX backend, which LM Studio ships natively on Apple Silicon and which outruns the Metal llama.cpp path for MLX-format models. Everywhere else, you are not choosing an engine. You are choosing a shape.
Three shapes
Ollama is a daemon, not an app. There's a CLI with a Docker-like pull/run feel, and behind it a background server on localhost:11434 speaking a native API, an OpenAI-compatible one, and even an Anthropic-compatible one. It supports tool-calling loops for agents. There is no chat window of its own worth speaking of — and that's the point. Ollama is the thing other things build on: an open-source (MIT) local-inference primitive that any app, script, or agent framework can target as a provider. If your end state is code calling a model, Ollama is the least-friction backend, which is exactly why it has become the default local provider that other tools assume.
Jan is the open app. It's a desktop chat application — model browser, conversations, the things a person clicks — that also runs a local OpenAI-compatible server on port 1337 for other software to use. The distinguishing fact is its license: Apache 2.0, open end to end. (A common secondary-source error calls it AGPL; the repo's LICENSE file says Apache 2.0.) Jan is for the person who wants the comfortable GUI of a desktop app and the auditability and freedom of open source, with privacy — everything runs offline on your machine — as the founding premise.
LM Studio is the third shape, and it's the one with a catch worth being precise about.
LM Studio is the polished closed app. It has the best desktop experience of the three: the slickest model browser for finding and downloading from Hugging Face, the cleanest chat and document UI, and a local server on port 1234 that speaks the OpenAI API (including the newer Responses API) with tool calling and first-class MCP support. On Apple Silicon it ships the native MLX backend, its real performance edge. As of mid-2025 it's free for both personal and commercial use. The catch: the desktop app is proprietary. Its developer tooling — the lms CLI and the TypeScript and Python SDKs — is open and MIT-licensed, but the application itself is closed. For many users that's a non-issue; for anyone with an open-source requirement, it's the whole decision.
How to choose
Don't choose on speed; you'd be measuring the same engine three times. Choose on what you're trying to do.
- You're building software — an agent, a script, a service — and want a local model behind an API: Ollama. It's headless by design, MIT, and the de facto local backend everything else already talks to.
- You want the nicest desktop experience and don't mind a closed app (and you're on Apple Silicon, where its MLX backend is a genuine speed win): LM Studio.
- You want a desktop app but need it fully open source and offline-first: Jan.
A useful tell: these aren't strictly rivals. A common setup is Ollama as the always-on backend that agent code targets, with LM Studio or Jan as the GUI you open to eyeball a new model before wiring it in. The mistake is treating the choice as a benchmark race. It's a question about what you want the tool to be — and on that question, the three give clearly different answers.
For choosing the server-side inference engine when you outgrow a laptop, see vLLM vs SGLang vs Ollama; for the build-vs-buy framing on running your own models at all, local vs Claude.
