Best Ollama GUI: Which Front-End to Use on Each Platform

I measured what an Ollama GUI costs in memory on an 8 GB M1 MacBook Air. Open WebUI's Docker container held 989.7 MiB while idle and peaked at 990.2 MiB during a chat, a fixed cost of just under 1 GiB on a machine that only has 8. The Hollama server used 17.37 MiB, because its chat runs in the browser. On a low-memory Mac, a lightweight front-end leaves that memory for the model.

The best Ollama GUI for most people is Ollama's own desktop app on Mac and Windows: it ships with Ollama, so there is nothing else to install. Pick Open WebUI for a shared or multi-user server, Page Assist for a browser extension, AnythingLLM for chatting with documents, and Enchanted or Chatbox for a phone. Every one of them talks to the same local Ollama server on port 11434, so you can try two without downloading your models again.

An Ollama GUI, also searched as an Ollama UI or Ollama web UI, is a front-end: a chat interface that sits on top of the Ollama runtime and sends your prompts to it. The runtime does the inference; the front-end adds features such as document chat, user accounts, or MCP tools. The matrix below compares nine front-ends, with licenses and release dates as of September 30, 2026. The memory figures come from my own run on October 5, 2026; the feature rows come from each project's documentation.

Does Ollama have a GUI? What the official app does

Yes. Since July 2025, Ollama's installer for macOS and Windows includes a desktop app that downloads models and chats with them. It accepts dragged-in text files and PDFs, sends images to vision models, and exposes a context length setting for longer documents, at the cost of more memory.

The app has clear limits, and they explain why third-party front-ends still exist. Linux gets no desktop app: the Ollama Linux docs give the curl -fsSL https://ollama.com/install.sh | sh script plus ollama serve, which installs the server and CLI only. The app is also a single-user local tool with no accounts, no persistent knowledge base, and no MCP client. The request to add Model Context Protocol support has been open since November 2024.

To start the Ollama GUI, launch Ollama from Applications on a Mac or the Start menu on Windows. The same install registers a login item, so Ollama may already be running. It serves the API at http://localhost:11434, which is also the address every other front-end in this guide connects to. Ollama itself does not ship a browser-based web UI. The official app is a native window, and web UIs such as Open WebUI are separate projects that connect to the same server. If you prefer the terminal, the Ollama commands cheat sheet covers the CLI side.

How much memory an Ollama GUI uses on an 8 GB Mac

A front-end does not change which models fit your hardware, but it does take memory of its own, and on an 8 GB Mac that memory comes out of the same pool the model needs. I ran Open WebUI v0.11.4 in Docker, then Ollama 0.35.1 on its own, then Hollama 0.35.4, on an M1 MacBook Air with 8 GB of unified memory, and sent the same prompt to the same small model through Open WebUI and straight to Ollama's API.

The Open WebUI number barely moves. The container sat just under 1 GiB with nobody using it and stayed there while it served a chat, so the cost is fixed: you pay it for as long as the container runs, not only while you chat. That is roughly the same amount of memory Ollama needed to serve the model itself. Ollama's figures are ps RSS for ollama serve plus its llama-server runner, which counts the model file's memory-mapped pages; macOS's footprint tool, which leaves those pages out, put the runner and server at 204 MB plus 15 MB. ollama ps reported the loaded model at 1.2 GB, running 100% on the GPU, in both chats.

I would not read the gap between the two Ollama peaks as a cost Open WebUI adds. The two runs sampled memory at different intervals on a Mac that was already swapping, and a single chat each is too few to separate a real difference from noise.

Hollama is a different design. Its Docker image is a small Node server, 223 MB on disk, that hands a chat app to your browser, so its 17.37 MiB is the server alone. The memory of the browser tab running the chat was not measured, for Hollama or for Open WebUI.

Not every GUI on this page was tested. The Ollama desktop app, Page Assist, AnythingLLM, Msty Studio, Chatbox, Enchanted, and Reins all need an app-store, extension-store, or GUI install, and none of them is in these numbers.

On an 8 GB machine, I would start with a front-end that adds no server of its own: the Ollama app on a Mac or Windows PC, or Page Assist in the browser you already have open. The reason is the table above. Open WebUI in Docker holds just under 1 GiB whether or not you use it, which on an 8 GB machine is memory a larger model or a longer context could have used. If you want Open WebUI on a small machine anyway, stop the container when you are not chatting. Open WebUI remains the right pick for a shared server, where that fixed cost buys accounts, roles, and Knowledge Bases.

Ollama GUI selection matrix

Read it left to right: rule out anything that does not run where you need it, check the Docker column, then match the one extra capability you need. "Not listed" means the project's README or docs do not document the feature; it does not prove the feature is absent.

The rows come from each project's own documentation and GitHub releases page, not from other roundups. The license column matters more than most lists admit: one popular ranking page lists Open WebUI as MIT, while the project's license page says the terms changed with v0.6.6 in April 2025.

Ollama GUI ports and URLs

Most connection problems come from pointing a front-end at the wrong address. These are the defaults each piece uses:

A GUI on another machine or a phone needs the Ollama server's LAN address, not localhost, because localhost always means the device the GUI runs on.

Best Ollama GUI by use case

The fastest route to the right front-end is to name the one capability you need beyond chat. This table maps common situations to a first pick and a fallback.

In my view the Ollama app wins the single-user case because it removes a second install, not because it has more features. Once you need accounts, a knowledge base, or tools, a third-party front-end is the better fit.

Ollama GUI on Linux and Ubuntu

Linux is the one desktop platform with no official Ollama app, so every Linux GUI is third-party. Four routes work on Ubuntu and most other distributions:

• Web UI in any browser: Open WebUI through pip install open-webui on Python 3.11 or 3.12, or through Docker.
• Desktop app: AnythingLLM Desktop publishes a Linux build, and Chatbox ships Linux releases on its GitHub page.
• Flatpak: Reins is listed on Flathub for a native Linux window.
• No install at all: Page Assist in Chrome or Firefox talks to the local server from the browser.

Open WebUI: the Ollama web UI for teams

Open WebUI is a self-hosted web interface that connects to Ollama and other model servers, and it is the default answer when more than one person needs access. Administrators get role-based access control with three system roles plus groups and per-feature permissions. Users get Knowledge Bases for asking questions across uploaded documents, and since v0.6.31 the server can call MCP tools natively.

You do not have to use Docker. The Open WebUI docs call Docker "officially supported and recommended," but they also document pip install open-webui followed by open-webui serve, which serves the UI on http://localhost:8080. The Python route needs Python 3.11 or 3.12; 3.13 is not supported yet. If you do use the container, the :slim image is about 176 MB against 1.66 GB for :main. For the Apple silicon (arm64) build of v0.11.4 on October 5, 2026, the registry layers summed to 175.9 MB for :slim and 1,492.1 MB for the full image, which took 5.85 GB of disk once pulled. The memory figures earlier on this page are for the full image; the :slim image and the pip route were not part of that run.

Check the license before you rebrand it. Since v0.6.6, the Open WebUI License adds a clause that forbids removing the "Open WebUI" name and logo unless the deployment has 50 or fewer users in a rolling 30-day window, you have written permission, or you hold an enterprise license. Code up to v0.6.5 remains BSD-3-Clause.

If you already run it in a container, two companion guides pick up from here: the Open WebUI Docker update guide covers keeping it current without losing chats, and Ollama Docker GPU setup covers wiring the container to Ollama with GPU access.

Page Assist: an Ollama UI inside your browser

Page Assist is an open-source browser extension that opens a sidebar on any webpage and a full-tab web UI, both pointed at your local Ollama. According to the Page Assist docs, it installs from the Chrome, Firefox, and Edge stores and also runs on Brave. Because it lives in the browser, it needs no Docker, no Python, and no port of its own.

It does more than chat. The Knowledge Base feature accepts PDF, DOCX, TXT, CSV, and Markdown files, but it stores embeddings in browser storage, and the docs warn this may cause performance issues with large sets. They recommend an embedding model such as nomic-embed-text rather than a chat model for that job.

MCP support connects the extension to remote servers over Streamable HTTP, with bearer or OAuth 2.1 auth, and a setting that asks for approval before any tool runs. Turn that setting on for servers that can write files or send messages. Support is still maturing: an open Page Assist issue from September 2026 reports MCP tools registering without requests ever reaching the server.

AnythingLLM and Msty for document chat

AnythingLLM is an all-in-one app built around document workspaces: you add files, it embeds them, and you chat against them with Ollama as the model provider. It also runs agents and is MCP-compatible. The desktop build covers Mac, Windows, and Linux, but the AnythingLLM README marks multi-user support and permissioning as "Docker version only," so a shared install means running the server edition.

Pick the desktop build for a private library on your laptop and the Docker build when several people share workspaces. Both are MIT licensed.

Msty Studio covers similar ground in a closed-source desktop app that can drive Ollama, llama.cpp, or MLX. Its Free tier costs $0 and includes chat with local models, while Knowledge Stacks for RAG and MCP tools are marked limited on Free. The Aurum tier costs $149 per user per year, according to Msty's pricing page. I would choose Msty only if you want its polish and accept a proprietary license, since AnythingLLM does the document job under MIT.

Mobile and Apple-native Ollama clients

Phones cannot run your desktop's models, so a mobile Ollama UI is a remote control for an Ollama server on your network. Ollama binds to 127.0.0.1 by default, so set OLLAMA_HOST on the server machine before a phone can reach it, and point the app at that machine's LAN address rather than localhost.

On Apple devices, Enchanted is the native option: an open-source Swift app for macOS, iOS, and visionOS, distributed through the App Store under Apache-2.0. It needs your own Ollama server. Its README now points to a successor project called Jaz, and its last release shipped in June 2026, so treat it as stable rather than fast-moving.

On Android, Chatbox publishes an APK alongside its Windows, Mac, Linux, and iOS builds, and Reins ships Android and Windows releases next to its App Store and Flathub listings. Reins focuses on per-chat settings such as system prompt, temperature, and context size.

The zero-install route is Open WebUI itself. Every Open WebUI instance is a Progressive Web App, so you can add it to a phone's home screen from the browser and get a full-screen app window with no separate download.

Lightweight options and what is not an Ollama GUI

The project literally named ollama-ui is a simple HTML UI for Ollama. Its repository has no tagged releases and was last updated in April 2026, so treat it as a minimal option rather than a maintained app.

If Open WebUI feels heavy, Hollama is a minimal chat app that runs entirely in your browser and stores data locally. It supports Ollama and OpenAI-compatible servers, several servers at once, and model downloads from the UI, and it also offers desktop builds and a Docker image. Its last release was 0.35.4 in October 2025, which suggests slower maintenance than the leaders. It's FOSS includes it among 12 web UI tools for Ollama, alongside options such as Lobe Chat and LibreChat that cover similar chat-first ground.

LM Studio is not an Ollama front-end. It is a separate runtime with its own model downloads, so it replaces Ollama rather than sitting on top of it; see LM Studio vs Ollama for that choice, or Ollama alternatives for the other runtimes. Whichever GUI you choose, it does not change which models fit your hardware. For that, use the LLM VRAM calculator and the best Ollama model for coding picks.

How I tested this

• Machine: a MacBook Air with an Apple M1 and 8 GB of unified memory, with desktop apps open. Docker Desktop's Linux VM was capped at just under 4 GiB.
• Date: October 5, 2026.
• Versions: Ollama 0.35.1, the native macOS build running on Metal; Open WebUI v0.11.4 from the ghcr.io/open-webui/open-webui image, connected to the host's Ollama through host.docker.internal, holding one admin user and one earlier chat; Hollama 0.35.4 from the ghcr.io/fmaclen/hollama image.
• Model: one small Qwen model in Q4_K_M, the same for both chats.
• What was run: with the model unloaded and no browser tab open, I left Open WebUI idle for a minute and took three docker stats samples ten seconds apart. Then I sent one chat request through Open WebUI's /api/chat/completions endpoint while sampling the container and the Ollama processes every few seconds. Next I stopped Open WebUI, unloaded the model, sampled idle Ollama, and sent the same prompt straight to Ollama's /api/chat while sampling about once a second. Last, I pulled Hollama, confirmed it served its page, sampled docker stats after it sat idle, and removed it. Container memory is docker stats MEM USAGE; Ollama memory is ps RSS of ollama serve plus its llama-server runner.
• Caveat: the Mac was already under memory pressure, with about 11 GB of swap in use before the first capture. Each chat ran once. Treat the Ollama peaks as approximate; the Open WebUI container figure held steady across every sample.
• Not tested: the Ollama desktop app, Page Assist, AnythingLLM, Msty Studio, Chatbox, Enchanted, and Reins; browser tab memory for Open WebUI or Hollama; Open WebUI installed with pip or the :slim image; larger models; and Linux or Windows hosts.
• Raw record: every metric is in the public results file, with the capture and derivation scripts in the same folder.

FAQ

Is Ollama free to use?

Yes, running models locally is free: Ollama is open source under the MIT license. Its optional cloud models are the paid part, with a Free tier of starter credits, Pro at $20 per month, and Max at $100 per month, according to Ollama's pricing page. Most front-ends here are free; Msty Studio's Aurum plan costs $149 per user per year.

Related coverage

• Open WebUI Docker Update: Backup, Pinning, and Rollback
• Ollama Docker GPU: Fix CPU Fallback and Open WebUI
• Best Ollama Model for Coding: Picks by VRAM and Agent Tool
• Ollama Commands: CLI Cheat Sheet, Env Vars, Update and Uninstall

References

• AnythingLLM README - https://github.com/Mintplex-Labs/anything-llm
• It's FOSS - https://itsfoss.com/ollama-web-ui-tools/
• Ollama blog - https://ollama.com/blog/new-app
• Ollama docs - https://docs.ollama.com/linux
• Open WebUI docs - https://docs.openwebui.com/getting-started/quick-start
• Page Assist docs - https://github.com/n4ze3m/page-assist/tree/main/docs