Ollama and LM Studio are the two names that appear most frequently when someone begins researching how to run a language model on their own hardware. A third option, Jan, has grown into a serious contender. All three applications accomplish the same fundamental task. They download an open-weight model and load it into memory and make it available for conversation or integration with other software. The differences lie in philosophy and interface and intended audience, and understanding those differences makes choosing between them straightforward.
What is Ollama?
Ollama is a free open-source tool that manages local language models through a command-line interface backed by a persistent background server. It is available on Windows and macOS and Linux. Installation produces a small daemon that listens on port 11434 and accepts both terminal commands and REST API calls. Retrieving and running a model requires a single instruction. ollama run llama3.1 downloads the appropriate quantized file if it is not already present and opens an interactive session immediately afterwards.
The model library maintained by the Ollama project covers several hundred variants across families such as Llama and Qwen and Mistral and DeepSeek and Phi. Each entry is versioned and tagged with parameter sizes, so selecting a build appropriate to available hardware takes little guesswork. Ollama exposes a REST endpoint that mirrors the OpenAI API shape, so a large number of third-party tools such as editor extensions and chat front ends and agent frameworks can be redirected at a local model without code changes. The open-source project Open WebUIis the most widely used companion for those who want a browser-based chat interface rather than a terminal prompt. It connects to Ollama's server and provides a ChatGPT-like experience served entirely from your own machine. The full walkthrough for setting up this combination is covered in the guide to running AI locally with Ollama.
Ollama is oriented toward developers and technically confident users. There is no graphical application to open. The workflow is the terminal and the API and whatever front end you wire up separately. That absence of a bundled GUI is not a limitation so much as a design decision. It keeps Ollama lightweight and composable, which is exactly what server deployments and automation scripts require.
What is LM Studio?
LM Studio is a desktop application available on Windows and macOS and Linux that brings model discovery and download and conversation into a single graphical window. It is free for personal use and closed-source. The central workflow is visual. A built-in Discover tab connects to Hugging Face and allows searching by model family or size and displays estimated RAM and VRAM requirements before any download begins. Selecting a model and clicking Download is the entirety of the setup process.
Under the surface LM Studio uses llama.cpp as its inference engine on all platforms and additionally supports Apple's MLX framework on Apple Silicon, which gives it strong performance across hardware types without user configuration. A Developer mode starts a local API server on port 1234 that is OpenAI-compatible and allows external tools to connect to the local model just as they would to a cloud endpoint. A built-in chat playground and document attachment for simple question-answering over files and fine-grained parameter controls such as temperature and context length and repeat penalty are all accessible through the same interface.
LM Studio suits users who want substantial control over inference settings and prefer to manage everything through a GUI rather than a terminal. It is not the most beginner-friendly of the three. The range of exposed options can initially overwhelm someone who only wants to start a conversation. But for a technically minded user who has no interest in the command line it offers the deepest graphical configuration available. The application is closed-source, so users who place significant weight on software transparency may prefer one of the open alternatives.
What is Jan?
Jan is a fully open-source desktop application released under the AGPLv3 licence that targets users who want the polish of a graphical interface alongside the reassurance of inspectable code. It runs on Windows and macOS and Linux. Like LM Studio, Jan presents a straightforward model browser and a built-in chat interface. Like Ollama, its source code is publicly available and auditable. This combination of an approachable GUI and an open licence and offline-first design positions Jan as the option most similar in spirit to "a private ChatGPT that anyone can install."
Jan uses llama.cpp for inference and supports GPU acceleration through CUDA on NVIDIA hardware and Metal on Apple Silicon and ROCm on supported AMD cards. A notable feature added and refined through 2025 is native support for the Model Context Protocol (MCP), which allows Jan to connect local models to external tools such as web search and file access and custom APIs without requiring you to write code. Conversation history persists across sessions by default, and the application can also relay requests to remote providers such as OpenAI or Anthropic, which makes it function as a unified chat front end for both local and cloud models. If you consider your model runner a long-term extensible part of your workflow, Jan's plugin architecture supports that ambition.
The primary trade-off relative to LM Studio is that LM Studio's inference tuning controls are more granular. Relative to Ollama, Jan does not carry the same breadth of third-party integrations. Jan is however the only one of the three that is simultaneously graphical and fully open-source and natively extensible through a plugin system, a combination that appeals strongly to users who want transparency without sacrificing convenience.
Ollama vs LM Studio vs Jan: how to choose
The right tool depends less on which is "best" in the abstract and more on what you are trying to accomplish:
- Developers and scripters should use Ollama. The REST API and the composable CLI and the straightforward server model and the wide ecosystem of third-party integrations make it the default for anyone writing code around a local model. Add Open WebUI if a browser interface is wanted.
- Non-technical users who want a familiar chat experience should use Jan. One-click downloads and persistent conversations and an open licence and no terminal requirement make it the closest equivalent to "ChatGPT running locally" for general audiences.
- Users who want deep inference control through a GUI should use LM Studio. The model discovery interface is the most refined of the three, the parameter controls are the most comprehensive, and the dual llama.cpp and MLX engine support makes it the strongest single-application choice for Apple Silicon users who want graphical control.
- Privacy- and transparency-conscious users should use Ollama or Jan. Both are fully open-source and LM Studio is not. All three operate entirely offline once a model is downloaded.
- Users running models on a headless server should use Ollama. It is the only one of the three designed for server deployment, with lower resource overhead and better multi-model memory management in that context.
Hardware remains the constraint that no software choice can override. Which models fit in memory and how quickly they respond depends entirely on available VRAM and on whether a model has been appropriately quantized for the card in question. The WillMyGPURunIt calculator provides model-specific estimates for a given build before any download is attempted, the sensible first step regardless of which application you choose.