5 Best Apps to Run Local AI Models on Your PC or Mac

LM Studio is a sensible first stop if you want to download a model and chat through a graphical interface. Choose Ollama when other apps need a local AI backend, Jan for an open-source desktop chat experience, GPT4All for straightforward document collections, or AnythingLLM for a more organized personal knowledge workspace.

These apps mainly run language models for writing, coding assistance and document questions. They are not interchangeable with image-generation software such as ComfyUI. The app manages the model; the model determines much of the answer quality. Installing a different interface will not turn a small model into a frontier cloud system.

Updated October 14, 2026

Which local AI app should you choose?

Choose by the job you need to finish, not by the longest model list. All five offer a free route to local inference, but hardware, electricity, optional cloud services and individual model licenses still matter. This is a use-case comparison, not a measured speed ranking.

5 apps worth installing

1. LM Studio — the easiest place to start comparing models

LM Studio brings model discovery, downloads, chat and a local server into one interface. It supports GGUF through llama.cpp and MLX on Apple Silicon. That makes it useful when you want to try two small models, compare their answers and adjust context length without assembling a separate backend. You can also attach documents for local questions.

Check which LM Studio product and model route you are using: the current website also promotes Bionic and optional cloud inference. Local model use is included in the free tier; a cloud subscription is not required for ordinary local chat. A cloud model or web-connected tool remains an online service even when the surrounding app is installed on your laptop.

Official website · Setup documentation

Getting started

1. Install the correct build from the official download page. On Mac, check Apple Silicon support; the documented LM Studio desktop requirements exclude Intel Macs.
2. Use model discovery to download a small supported instruction model. Start with a quantized version that leaves memory free for your operating system.
3. Load it in chat and ask a short question before attaching a document. For another application, start the local server and copy the endpoint shown by your installed version.

Best for readers who prefer buttons to terminal commands. The official requirements recommend 16 GB RAM and require macOS 14 or newer on supported Macs; Windows x64 also requires AVX2. Treat these as app requirements, not a guarantee that a large model will fit.

2. Ollama — the most useful backend for connected apps

Ollama is a practical choice when you want one model runtime behind several tools. Its local API is commonly used by document workspaces, coding assistants and custom scripts. Windows and Mac users can install a native app rather than starting with Docker. A desktop interface is available, so describing Ollama as command-line-only would be misleading.

Its main advantage is reuse: keep the runtime running, then connect a compatible client to it. The trade-off is that a polished document library or specialized workflow may live in that client, not in Ollama itself. Choose a local model explicitly; models marked for cloud use run remotely. Local inference needs no cloud API key.

Official website · Setup documentation

Getting started

1. Install Ollama, then choose a local model from the app or official model library. Confirm its download size and hardware fit.
2. For an existing local download, use the commands below to list models, run the exact name and inspect CPU/GPU placement. Replace MODEL_NAME with a name from the list.
3. Connect your client to the local endpoint, normally http://localhost:11434. Keep it bound to your own computer unless you deliberately configure a secured network service.

ollama list
ollama run MODEL_NAME
ollama ps

Intel Mac users have a documented CPU-only route on macOS 14 or newer; Apple Silicon supports GPU acceleration. CPU-only operation can be much slower. For strict local-only operation, set OLLAMA_NO_CLOUD=1 and restart Ollama, as described in its FAQ.

3. Jan — an open-source desktop alternative

Jan suits people who want a familiar chat window with local model management and an open-source application. Its Hub helps you find downloads, and current model management shows hardware-fit guidance. Local llama.cpp models and Apple Silicon MLX options give you different execution routes; use the format supported by the selected engine.

The same app can also connect to remote providers. That flexibility is useful, but it makes provider selection important: a familiar model name in a desktop menu does not prove the request stays on your device. Jan also offers a local API server. Copy the address from your version’s settings or documentation instead of assuming an old tutorial’s port still applies.

Official website · Setup documentation

Getting started

1. Install Jan for your OS and open Hub. Choose a small local model marked as fitting your hardware, then finish its download.
2. Select that downloaded model in a new chat. Test a short writing or summarization prompt before adding tools.
3. If you need API access, configure the local server and use its displayed endpoint. For offline use, leave remote model providers and online tools out of the workflow.

Choose Jan when open-source desktop software matters to you. If loading fails, check the model architecture and inference backend before repeatedly downloading different quantizations.

4. GPT4All — a straightforward route to local document questions

GPT4All combines a desktop chat app with LocalDocs, which turns selected folders into searchable collections for a model. It is a useful starting point for product manuals, personal notes or a small research folder when you do not need a team server. You download a compatible model and choose the relevant collection in a conversation.

Retrieval is not the same as teaching the model every page permanently. The app finds relevant excerpts and passes them into the answer context. A missing passage, weak embedding match or unreadable scan can therefore produce a poor answer even when the language model itself runs correctly. Start with a small folder whose contents you know.

Official website · Setup documentation

Getting started

1. Install GPT4All, open Models and use Add Model to download a compatible model. Load it in Chats and check ordinary conversation first.
2. Create a LocalDocs collection from a small folder and wait for indexing to finish. Make sure the required local embedding model is downloaded too.
3. Select the collection in chat and ask a question with a known answer. Inspect the supporting excerpts; do not treat an unsupported answer as a fact from the document.

The name does not mean OpenAI GPT-4 runs on your computer. It is an application for supported models. Check the runtime’s architecture support before importing a new GGUF file; the file extension alone is not enough.

5. AnythingLLM — best for a personal document workspace

AnythingLLM organizes work around workspaces: for example, one for project specifications and another for study material. It combines chat, document retrieval and optional agent functions. The Desktop edition includes a local model route, or you can connect a local runtime such as Ollama. This makes it a better fit for a growing document workflow than for merely trying one chatbot.

Keep the architecture clear. The chat model, embedding provider and storage settings all affect where information goes. Selecting a local chat model while using a remote embedding API is not a fully local document pipeline. The desktop app is designed for a single user; a shared team deployment belongs to the separate server/Docker route.

Official website · Setup documentation

Getting started

1. Install the Desktop edition and choose local model and embedding providers. If using Ollama, start it first and select an available local model.
2. Create a workspace, add a few readable documents and complete the embedding/indexing step. Keep unrelated projects in separate workspaces.
3. Ask for an answer with source passages. Add agent tools only after basic retrieval works and review the access each tool requires.

Best for readers who value organized document collections. A workspace separation helps retrieval, but it should not be mistaken for the account and permission controls of a multi-user server.

What hardware do you actually need?

The following are conservative starting suggestions, not vendor minimums or promises of speed. Free memory, model architecture, quantization and context length matter more than the app’s download size. A model file must fit alongside its working memory and the rest of your applications.

Q4 reduces weight precision to save memory; it does not mean a model requires exactly four gigabytes. Longer context adds memory demand. For speed comparisons, use the same model, quantization, prompt and context on the same computer. A tokens-per-second number from another machine is not a reliable buying promise.

Your first local AI session

1. Pick one app and one task: a short draft, a code explanation or a question about a small document. Do not install all five first.
2. Download the app, runtime, language model and any embedding model while connected. Check available disk space; keeping multiple quantizations can duplicate many gigabytes.
3. Use a small instruction-tuned model, a modest context window and default generation settings. Ask it to rewrite a short paragraph while preserving names and dates.
4. For document work, test a file with selectable text. Ask for a specific fact and its supporting passage, then verify that passage yourself.
5. Disconnect from the network and repeat the task in a new chat. Successful generation is a useful offline check; it is not a complete security audit. Reconnect when downloads or web features are needed.

Using only the supplied text, list the decisions, explicitly stated deadlines and unresolved questions. Quote a short supporting passage for each item. If the text does not contain an answer, say so. Do not invent dates or commitments.

Offline use, privacy and real costs

Local inference avoids a provider’s per-token bill, but you still pay for the computer, power and storage. Optional remote APIs, hosted services and cloud subscriptions are separate. All five have a free local route, so there is no need to buy a subscription just to try a small offline model.

Privacy depends on the whole workflow: model endpoint, embeddings, web search, connectors, logs and synced folders. Local chat history stored in a cloud-synced directory can still leave the computer. Keep confidential work in a location and configuration approved for that data.

Local execution does not remove a model’s license or change its refusal behavior. Roleplay and adult-content handling depend on the particular model and any connected service, not on the launcher’s name. Check both app and model terms before commercial use.

Common problems and practical fixes

The model will not load

Check supported architecture, file format and required runtime first. Then try a smaller quantization, reduce context and free memory. A GGUF file can still target an architecture your runtime does not support.

Answers appear slowly

Separate initial model loading from generation speed. Check GPU use, power-saving mode and memory pressure. With Ollama, inspect ollama ps; a CPU fallback may explain the difference.

Document answers ignore the file

Confirm indexing finished and the correct collection/workspace is selected. Scanned PDFs may need OCR first. Ask about a known passage before adding hundreds of files.

The local API is unreachable

Confirm the server is running, the port matches and the model is loaded. A client inside Docker has a different localhost from the host computer; follow that client’s networking instructions.

Frequently asked questions

Can I use these apps without an internet connection?

Yes, with a fully local configuration after all required downloads. Model search, cloud models, web browsing and remote connectors still need a connection. Download embedding models as well if you want offline document retrieval.

Do I need an NVIDIA graphics card?

No. Small models can run on a CPU, and supported Apple Silicon, AMD or Intel GPU paths vary by app and backend. A supported GPU usually improves responsiveness, but hardware compatibility must be checked for the exact build.

Which app should I choose for an Intel Mac?

Do not assume every Mac download supports Intel. LM Studio’s documented desktop requirements exclude Intel Macs, while Ollama documents CPU-only support on compatible Intel Macs running macOS 14 or newer. Check the current installer and OS requirements before downloading a large model.

Will a different app make the model smarter?

Usually the model, quantization and prompt matter more. Interfaces can improve retrieval, templates and tool use, but cannot guarantee correct answers. Compare outputs on your own tasks rather than ranking apps by the models they display.

Can I generate images and video with these apps?

This selection focuses on local language-model workflows. Some models can understand images, which is different from generating them. For image/video generation, use a compatible generation workflow such as ComfyUI and check its separate GPU and model requirements.

Can I connect one runtime to another app?

Yes, when their API formats are compatible. For example, a document client can use Ollama as its local model provider. Check base URL, model name and supported features; an OpenAI-compatible endpoint does not imply every OpenAI feature is implemented.

Start with one app and one small model

For most beginners, start with LM Studio or Jan and a small model. Choose Ollama first if an integration already asks for it. Use GPT4All for a modest document collection, or AnythingLLM when you need distinct workspaces and more control over retrieval.

Keep the first test small enough that you can judge every answer. Once it works offline and fits comfortably in memory, increase model size or add documents one step at a time. If your main question is which model to download rather than which app to install, continue with the local model comparison below.

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