Ollama, LM Studio, and GPT4All all run local AI models privately on your own hardware, but they are built for different users and use cases. Ollama is a headless server designed for developers, homelab setups, and NAS Docker deployments. LM Studio is a polished desktop GUI app for non-technical users who want a point-and-click experience. GPT4All is the simplest entry point with the smallest footprint. Choosing the wrong tool for your setup creates unnecessary friction.
In short: Choose Ollama if you want a NAS Docker deployment, REST API access, or integration with Open WebUI or home automation. Choose LM Studio if you want a polished desktop GUI, easy model browsing, and do not need a persistent server. Choose GPT4All if you want the simplest possible setup with no configuration, and a limited model selection is acceptable.
Ollama
Ollama is an open-source local LLM runner built on llama.cpp. It exposes a REST API compatible with the OpenAI API spec, making it a drop-in backend for tools that support OpenAI endpoints. It runs as a background service, supports NVIDIA CUDA and AMD ROCm GPU acceleration, and has an official Docker image that makes it the default choice for NAS and homelab deployments.
The model library is broad and well-maintained. Ollama supports all major open model families: Llama (Meta), Mistral, Gemma (Google), Phi (Microsoft), Qwen (Alibaba), DeepSeek, and many more, all pulled by a simple ollama pull [model] command. Models are stored locally in GGUF format and can be versioned and swapped without reinstalling the application.
Ollama pairs naturally with Open WebUI, which provides a ChatGPT-style interface over Ollama's API. This combination (Ollama + Open WebUI) is what most NAS Ollama guides are actually setting up.
| Platform | macOS, Linux, Windows (WSL2) |
|---|---|
| Interface | CLI + REST API (no native GUI) |
| Docker support | Yes (official image: ollama/ollama) |
| GPU acceleration | NVIDIA CUDA, AMD ROCm |
| API compatibility | OpenAI-compatible REST API |
| Model format | GGUF (custom Modelfile wrapper) |
| Licence | MIT |
| Cost | Free |
Pros
- Native Docker deployment for NAS and homelab
- OpenAI-compatible API enables integration with hundreds of tools
- Broad model library, simple pull/run commands
- Runs as background service, persistent across reboots
- GPU acceleration works reliably on supported hardware
- Active development, frequent model family additions
Cons
- No native GUI. Requires Open WebUI or similar for a chat interface
- CLI setup may be unfamiliar for non-technical users
- API server exposes a port that needs to be secured on networked deployments
- Model management is command-line by default
LM Studio
LM Studio is a desktop GUI application for macOS, Windows, and Linux that makes downloading, managing, and chatting with local AI models as intuitive as a media player. It handles model discovery (via Hugging Face integration), download management, quantisation selection, and chat in a single polished interface. For non-technical users who want to run a local LLM without touching a command line, LM Studio is the easiest path.
LM Studio's model browser lets you search Hugging Face directly within the app, compare quantisation sizes, and download with a progress bar. The chat interface supports multiple conversation threads, system prompt customisation, and temperature/parameter controls exposed via sliders rather than config files.
LM Studio added a local server mode (Developer tab) in recent versions, exposing an OpenAI-compatible API. This bridges the gap with Ollama for users who want to use LM Studio as a backend for other tools. However, the server is tied to the desktop app being open, which limits its utility for always-on deployments like NAS.
| Platform | macOS, Windows, Linux |
|---|---|
| Interface | Native desktop GUI |
| Docker support | No (desktop app only) |
| GPU acceleration | NVIDIA CUDA, AMD (via Vulkan), Apple Metal |
| API compatibility | OpenAI-compatible (server mode, requires app running) |
| Model format | GGUF (Hugging Face integration) |
| Licence | Proprietary (free for personal use) |
| Cost | Free for personal use; commercial licence required for business use |
Pros
- Best user experience for non-technical users
- Integrated Hugging Face model browser and downloader
- Polished chat interface with conversation management
- Apple Metal GPU acceleration works on Mac
- No command line required for basic use
- Server mode enables API access from other tools
Cons
- No Docker deployment, not suitable for NAS or headless servers
- Server mode requires the desktop app to be running (not suitable for always-on)
- Proprietary licence limits commercial use
- Heavier application footprint than Ollama
- Model management stored in app-specific directories can complicate migrations
GPT4All
GPT4All is the original consumer-friendly local LLM application, built by Nomic AI. It is a desktop GUI app with a focus on maximum simplicity and minimum dependencies. The application runs without Docker, without configuration files, and without an internet connection once models are downloaded. For absolute beginners who want to try local AI with no setup friction, GPT4All delivers on that promise.
The trade-off is scope. GPT4All's model library is curated rather than comprehensive, prioritising tested, verified models over the full open-model ecosystem. The API server mode was added in later versions but remains less developed than Ollama's. Performance for equivalent models is similar, as both use llama.cpp under the hood. GPT4All has not kept pace with the development velocity of Ollama and LM Studio in 2025-2026.
| Platform | macOS, Windows, Linux |
|---|---|
| Interface | Desktop GUI |
| Docker support | No |
| GPU acceleration | NVIDIA CUDA (limited), CPU-first design |
| API compatibility | Basic REST API (limited OpenAI compatibility) |
| Model format | GGUF (curated model library) |
| Licence | MIT |
| Cost | Free |
Pros
- Simplest installation of the three options
- Fully offline after model download
- Open source (MIT)
- Low system requirements. Works on older hardware
Cons
- Smallest model library of the three
- Less active development compared to Ollama and LM Studio
- No Docker support; not suitable for NAS or server deployments
- GPU acceleration less reliable and less tested than Ollama
- API server less capable than Ollama's
- UI has not kept pace with LM Studio's polish
Head-to-Head Comparison
Ollama vs LM Studio vs GPT4All
| Ollama | LM Studio | GPT4All | |
|---|---|---|---|
| Best for | NAS, homelab, developers | Desktop non-technical users | Absolute beginners |
| Interface | CLI + API | Desktop GUI | Desktop GUI |
| NAS/Docker deployment | Yes (official Docker image) | No | No |
| Always-on server | Yes | Requires app running | No |
| Model library breadth | Very broad (all major families) | Broad (via Hugging Face) | Limited (curated) |
| GPU acceleration | NVIDIA CUDA, AMD ROCm | NVIDIA, AMD (Vulkan), Apple Metal | NVIDIA (limited) |
| OpenAI API compatibility | Full | Partial (server mode) | Basic |
| Licence | MIT (free) | Free personal / paid commercial | MIT (free) |
| Active development | Very active | Active | Slower |
Which to Choose for Your Setup
For a NAS or homelab Docker deployment: Ollama is the only real option. It has an official Docker image, runs as a persistent service, and exposes a REST API that Open WebUI, Home Assistant, Obsidian plugins, and dozens of other tools can consume. Setting up Ollama on a Synology NAS or QNAP via Container Station uses the Ollama Docker image directly.
For a desktop Mac or Windows machine, non-technical user: LM Studio. The model browser and chat interface are genuinely excellent. If you later want to connect other tools to it, the server mode works for that. The commercial licence is worth noting if you are using it in a business context.
For the absolute first step into local AI with zero configuration: GPT4All. Download, install, pick a model, chat. No commands, no API configuration, no decisions required beyond model selection. Its limitations become apparent quickly, but as a starting point it is unmatched for simplicity.
For Mac users on M-series hardware: Both Ollama and LM Studio support Apple Metal acceleration on M1/M2/M3/M4. LM Studio's Metal support is particularly well-tested and produces excellent performance. Ollama on Mac also uses Metal via llama.cpp but is less visually obvious about doing so.
Australian Context: Cost and Privacy
All three tools are free for personal use and run entirely on local hardware. This is the core value proposition for Australian users: cloud AI subscriptions are USD-priced (ChatGPT Plus is USD$20/month, approximately AUD$30-32 at current exchange rates). Running equivalent quality locally via Ollama on hardware you already own eliminates this ongoing cost.
For data privacy, all three tools process queries locally. Documents you feed to a local model do not leave your network. For Australian businesses handling personal information under the Privacy Act 1988, this means AI-assisted document processing can occur without sending data to US-hosted cloud services, which simplifies compliance considerations for small practices handling sensitive client files.
The electricity cost of running Ollama 24/7 on a NAS or mini-PC is the main ongoing cost. Use the NAS power cost calculator to estimate your specific hardware's annual electricity cost at your state's rate.
Related reading: our NAS buyer's guide, our NAS vs cloud storage comparison, and our NAS explainer.
Free tools: NAS Sizing Wizard and AI Hardware Requirements Calculator — no signup required.
Can I use LM Studio as a backend for Open WebUI?
Yes. LM Studio's server mode exposes an OpenAI-compatible API endpoint. Open WebUI can connect to it by pointing to LM Studio's local address and port. The limitation is that LM Studio must be running on the desktop for the connection to work, unlike Ollama which runs as a background service. For a persistent always-on setup, Ollama is more reliable.
Is Ollama safe to run on a NAS?
Yes, with appropriate network configuration. Ollama's API server binds to localhost by default. If you expose it to your local network (needed for multi-device access), restrict access to your LAN via firewall rules and do not expose it to the internet. The Docker deployment via Container Station on QNAP or the Synology Docker package handles network binding. Never expose the Ollama API port directly to the internet without a reverse proxy and authentication.
Do all three tools use the same models?
All three support GGUF-format models, which is the standard format for quantised LLM inference. Ollama uses its own Modelfile format as a wrapper but downloads GGUF weights. LM Studio downloads directly from Hugging Face in GGUF format. GPT4All uses its own model repository. In practice, Ollama and LM Studio have access to the same broad universe of models via Hugging Face; GPT4All's curated library is smaller.
Which tool is fastest for inference?
For equivalent hardware and the same model and quantisation level, the difference is negligible. All three use llama.cpp as the inference engine. GPU acceleration makes a larger difference than tool choice. On CPU inference, Ollama's performance is well-optimised due to its upstream llama.cpp maintenance and BLAS backend support. In practice, you will not notice a speed difference when switching between Ollama and LM Studio on the same hardware.
Can I run more than one of these at the same time?
Yes, but only one tool can hold a model loaded in memory at a time unless you have enough RAM for multiple model instances. Running both Ollama and LM Studio simultaneously with different models loaded will consume RAM equal to the sum of both models. On a 16GB system, this is likely to cause swapping. On 32GB+ systems, it is viable for dedicated model-per-tool setups.
Is GPT4All still worth using in 2026?
For absolute beginners wanting the simplest entry point, yes. For anyone planning to go beyond basic chat (home automation integration, document Q&A pipelines, NAS deployment), Ollama or LM Studio are better long-term investments of setup time. GPT4All's development velocity has slowed relative to Ollama, and its model library is narrower. It remains a valid first step, not a long-term platform.
Ready to set up Ollama on your NAS? The step-by-step guide for Synology covers Container Manager deployment, model selection, and connecting Open WebUI.
Ollama on Synology Setup Guide