Ollama vs LM Studio vs GPT4All: Which Local AI Runner to Use

Ollama (a free, open-source model runner) is the safest default for an always-on local model service, LM Studio (a desktop and server app for local models) is strongest for desktop use, and GPT4All suits people who prioritise a simple LocalDocs workflow. An LLM (large language model) is software trained to generate and interpret text. The runner is the engine room that loads the model onto your hardware and gives applications a way to use it.

A NAS (network-attached storage device) connects to a router and lets computers, phones and other devices access shared storage and services. Ollama remains the least complicated choice for most NAS deployments, but LM Studio is no longer desktop-only. Its llmster daemon materially changes this comparison.

The runner does not determine answer quality by itself. Model choice, quantisation, available memory, context length and hardware usually matter more. Quantisation reduces the precision of model weights to cut memory use, rather like compressing a large toolkit into a smaller case while accepting that some fine detail may be lost. See which LLMs fit in 16GB, 32GB and 64GB of RAM and the quantisation explainer before comparing performance.

Ollama

Ollama is the strongest fit for developers, homelab operators and users who want a local model available as a background service. It runs on macOS, Windows and Linux, provides its own application programming interface, or API, and has an official Docker image. An API is a defined doorway through which another application sends prompts and receives answers.

Ollama has both a native REST API and compatibility with parts of the OpenAI API. OpenAI compatibility means software written for familiar OpenAI-style endpoints can often be redirected to the local runner, although it does not mean every OpenAI feature or request field behaves identically. Ollama documents the supported endpoints and fields in its OpenAI compatibility reference.

Model and hardware support

Ollama can pull packaged models from its library or import supported GGUF and Safetensors models through a Modelfile. GGUF is a model file format designed for efficient local inference, functioning like a packed suitcase that keeps the weights and information needed by the runner together. A Modelfile is Ollama's recipe for selecting a base model and applying parameters, templates or adapters. The supported import paths are documented in the Ollama Modelfile reference.

GPU acceleration uses a graphics processor to perform model calculations faster than a general-purpose processor, like moving a repetitive job from one versatile worker to a large specialised team. Ollama documents support for Nvidia GPUs, AMD GPUs through ROCm, Apple GPUs through Metal and Vulkan support on Windows and Linux. Hardware support remains device and driver dependent, so check the current Ollama GPU list rather than assuming that every GPU from a supported brand works.

Interface and document handling

Ollama now includes a chat application on macOS and Windows, so describing it as entirely command-line based is outdated. That application can accept dragged-in text, PDF and code files. This is useful for direct file questions, but it is not the same as maintaining an indexed document library for repeated retrieval.

For a richer browser interface, user accounts or more substantial document workflows, pair Ollama with a separate frontend. The Ollama frontend comparison explains that layer without confusing it with the runner itself.

Strengths and limits

• The official Docker image provides the clearest container route in this comparison.
• The background service and API suit automation and always-on use.
• Native and OpenAI-compatible APIs support a broad range of clients.
• The model library and Modelfile workflow simplify repeatable deployments.
• Desktop file chat exists on macOS and Windows.
• A full multi-user interface or persistent document library still requires another application.
• GPU passthrough depends on the host. Ollama states that Docker Desktop on macOS cannot provide GPU acceleration to its container.
• Network access must be deliberately secured. A convenient local API should not be exposed directly to the internet.

Ollama's repository uses the MIT licence, but that licence covers Ollama rather than every model downloaded through it. Each model can have separate conditions. The current licence is published in the Ollama repository.

LM Studio

LM Studio offers the most complete desktop experience of the three, with model discovery, downloads, configuration, chat and document attachment in one graphical application. It supports macOS, Windows and Linux, with exact processor and operating-system requirements listed in the LM Studio system requirements.

Its major change is llmster, a standalone daemon that runs without the graphical application. A headless daemon is a background service that does not require a screen or desktop, like an appliance that continues working after its control panel is closed. LM Studio documents llmster for Linux servers, cloud systems, GPU rigs and other machines where a GUI is unnecessary.

That makes the old verdict that LM Studio cannot operate as an always-on server incorrect. The daemon can start on boot, load models and expose an HTTP server independently of the desktop interface. The vendor provides a Linux startup sequence for llmster.

Model, API and document support

LM Studio runs GGUF models through llama.cpp on its supported platforms. On Apple silicon, it also supports MLX models. MLX is Apple's machine-learning framework for its processors, comparable to a workshop designed around the tools and shared memory layout of Apple hardware.

LM Studio provides its native REST API plus OpenAI-compatible and Anthropic-compatible endpoints. The current native API can manage downloads, load and unload models, maintain stateful chats and configure authentication tokens. Its API documentation distinguishes native features from the compatibility endpoints.

The desktop app includes document chat using retrieval-augmented generation, or RAG. RAG searches relevant passages before asking the model to answer, like a librarian placing the most useful pages on the desk before a question is considered. LM Studio says local models, document processing and its local server can operate offline once the required files and runtimes have been downloaded. Its offline operation documentation also identifies activities that still require connectivity, including model search and downloads.

Strengths and limits

• The desktop app gives beginners a visual model browser and chat interface.
• Built-in offline document chat avoids adding a separate frontend for basic RAG.
• llmster supports true headless operation without the desktop app.
• Native, OpenAI-compatible and Anthropic-compatible interfaces support development workflows.
• GGUF works across supported platforms, while MLX is available on Apple silicon.
• The official llmster Docker image remains labelled a technical preview and CPU-only on x86 systems.
• LM Studio is proprietary software even though it uses and contributes to open-source runtimes.
• Its certified platform requirements are more specific than the availability of a generic Linux installer, which matters on older NAS processors.

LM Studio announced on 8 July 2025 that it was free to use at home and at work. Its current desktop terms, versioned 23 August 2026, grant use solely for personal and internal business purposes and list restrictions that include service bureau, application service provider and software-as-a-service use. That wording should be read directly in the current LM Studio app terms when the deployment will serve customers or another organisation.

GPT4All

GPT4All is an open-source desktop application focused on private local chat and document collections. It supports Windows, macOS and x86-64 Linux, while its current repository also lists a separate Windows ARM installer. The main interface is graphical, and the project also provides a Python SDK for developers.

Its distinctive feature is LocalDocs. LocalDocs indexes folders into collections and retrieves relevant text for a conversation. Current settings documentation lists text, PDF, Markdown and reStructuredText among its default allowed file extensions and allows local embeddings or an optional Nomic Embed API. The cloud embedding option is off by default according to the GPT4All settings documentation.

API, formats and acceleration

GPT4All uses GGUF models and provides a curated model list. Custom compatible GGUF files can also be used, but compatibility still depends on the architecture and chat template rather than the filename alone.

The desktop application has a local HTTP server implementing a subset of the OpenAI API. Its documented endpoints cover model listing, text completions and chat completions. The server listens only on localhost, meaning the same computer, and LocalDocs collections must be selected through the GUI rather than through an API request. These limitations are documented in GPT4All's local API server guide.

GPT4All supports CPU inference plus Metal on Apple silicon, CUDA on supported Nvidia hardware and Vulkan on supported Nvidia or AMD hardware. Backend and quantisation compatibility vary, so a model that runs on the CPU may not use every available GPU backend. The project's current system requirements should be checked before installation.

Strengths and limits

• LocalDocs provides a clear folder-based document workflow inside the desktop app.
• The application and core repository use the MIT licence.
• A visual interface and curated model selection reduce initial setup decisions.
• The Python SDK supports custom local development without requiring the desktop chat workflow.
• The desktop API implements only a subset of the OpenAI specification and listens on localhost.
• The official documentation does not present a current turnkey, supported Docker deployment for the desktop application.
• LocalDocs collection selection is not remotely controllable through the documented API.
• It is a weaker fit for a shared NAS service than either Ollama or LM Studio's llmster.

The MIT licence covers GPT4All code, not every model in its catalogue. The repository describes GPT4All as open-source and available for commercial use, but model licences still need to be checked separately in the GPT4All repository.

Ollama vs LM Studio vs GPT4All Comparison

The important trade-off is control against convenience. Ollama provides the cleanest service architecture, LM Studio now spans both desktop and server roles, and GPT4All provides a focused document-oriented desktop workflow with narrower server capabilities.

Which One Works Best on a NAS or Home Server?

Ollama is the lowest-risk choice for a conventional NAS container deployment because the vendor publishes and documents an official Docker image. That does not mean every NAS will run useful models quickly. Processor architecture, instruction support, memory capacity and GPU access still determine what is practical.

For Synology, start with the Ollama on Synology setup guide. QNAP users can follow the Ollama on QNAP guide. Check the broader local LLM on a NAS assessment before allocating storage or buying memory.

LM Studio can now run headlessly through llmster, so it is a valid home-server option when the machine meets its supported Linux and processor requirements. It is not, however, an official Synology package. Its Docker image is still a CPU-only x86 technical preview, making it a more conditional NAS choice than Ollama.

GPT4All is not the sensible default for a shared NAS service. The desktop server is bound to localhost, depends on the GUI application and exposes a limited API. A developer could build a headless service around its Python SDK, but that is custom implementation work rather than a supported appliance-style deployment.

A common home-lab split is to store model files and documents on the NAS while running inference on a mini PC or GPU workstation. This avoids expecting a storage-focused NAS processor to perform like an AI system. Compare that architecture in mini PC versus NAS for local AI or use the AI hardware selector.

Three Mistakes to Avoid

The first mistake is choosing the runner before checking model memory. A polished interface cannot make an oversized model fit. Estimate model weights, context memory and runtime overhead before committing to a platform.

The second mistake is treating OpenAI compatibility as complete interchangeability. Each runner implements a different set of endpoints and fields. Test the actual client, streaming mode, tools and authentication workflow required by the intended application.

The third mistake is assuming that installing a local runner guarantees every request remains local. Select a downloaded local model, disable optional cloud or sharing features that are not needed and test operation after disconnecting the internet. The runner's licence, the model's licence and any connected cloud service are also separate decisions.

Australian Buyers: What You Need to Know

For an always-on Australian server, electricity cost should be calculated from measured average power rather than its maximum power-supply rating. Annual energy use in kilowatt-hours equals average watts multiplied by 8.76. A server averaging 40W therefore uses 350.4kWh per year, and its annual running cost is 350.4 multiplied by the usage charge shown on the electricity bill.

This method accommodates different Australian tariffs without pretending there is one national rate. Measure the complete system at the wall, including storage and idle periods, then compare the result with the NAS versus cloud AI cost guide. A machine that sleeps between sessions can cost materially less than one holding a model in memory all day.

NBN upload speed is usually not the main bottleneck for plain remote text chat because the model remains on the server and only requests and responses cross the connection. It matters more with multiple remote users, file transfers or other traffic sharing the service. NBN currently lists 50Mbps wholesale upload for eligible Home Fast II and Home Superfast services on FTTP and HFC, while Home Standard can provide up to 20Mbps or 5-20Mbps depending on fixed-line technology. Retail plans and real performance can differ from those wholesale figures, as the NBN residential speed page explains.

CGNAT, or Carrier-Grade NAT, is an IPv4 address-sharing measure some ISPs use that prevents conventional inbound IPv4 port forwarding; remote access may still be possible through alternatives such as IPv6 or a secured overlay network. It resembles an apartment building sharing one street address, where unsolicited deliveries cannot identify the correct unit. Australian ISP guidance confirms that CGNAT can interfere with port forwarding and remote servers. Check the connection before planning direct inbound access.

Do not publish an unauthenticated runner API directly to the internet. Use a properly secured private network or authenticated gateway, keep the runner updated and restrict access to required devices. Remote access architecture is a security decision, not simply a port-forwarding task.

Recommendations by Use Case

There is no universal winner because each runner removes a different kind of friction.

• Choose Ollama for an always-on NAS, home server, automation backend or broad third-party integration. Its official container and service-first design reduce deployment work.
• Choose LM Studio for a Mac, Windows or Linux desktop where model discovery, visual controls and document chat matter. Choose its llmster daemon when those same runtimes need to move to a supported headless server.
• Choose GPT4All for a single-user desktop focused on LocalDocs and a curated, open-source application. Do not choose it as the default for a shared NAS endpoint.
• Choose LM Studio or Ollama on Apple silicon according to workflow. LM Studio offers the more visual experience and MLX support, while Ollama is better suited to scripts and persistent integrations.
• Choose Ollama or llmster for development work, then test the exact API calls used by the application instead of relying on the OpenAI-compatible label.

Before installing anything, complete this short check:

1. Identify whether the runner will live on a desktop, NAS or dedicated server.
2. Decide whether it needs to survive reboots and operate without a logged-in user.
3. Select a model that fits available RAM or VRAM with room for context.
4. Confirm the required API endpoint, tool-calling and authentication behaviour.
5. Check the runner licence and the selected model licence separately.
6. Measure wall power and secure remote access before leaving the service running continuously.

Related reading: our NAS buyer's guide, our NAS vs cloud storage comparison, and our NAS explainer.

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