What is the easiest way to run AI locally?

LM Studio and Jan are the easiest point-and-click apps: install, pick a model that fits your machine and start chatting offline. Ollama is just as simple to install and is the best choice if you also want other apps to connect to your local models.

How much RAM do I need to run an LLM locally?

8GB of memory runs small 1B to 4B models, 16GB is comfortable for models up to roughly 8B to 14B, and 24GB to 32GB (or a 24GB GPU) opens up 26B to 32B models like Qwen3.8-27B or Gemma 4 31B. The largest models, like DeepSeek V4, need workstation or server hardware.

Is running AI locally free?

Yes. Ollama, LM Studio, Jan, GPT4All, llama.cpp, the AnythingLLM desktop app and self-hosted Open WebUI are all free for local use, and open models like Qwen, Gemma, Mistral Small and DeepSeek are free to download. Some of these tools sell optional cloud plans, but you never need them to run models on your own computer.

What is the best open model to run locally in 2026?

For most people, a Qwen 3.x or Gemma 4 model sized to fit your memory is the best starting point. Gemma 4 E4B suits laptops with 8GB to 16GB, while Qwen3.8-27B or Gemma 4 31B are strong choices on machines with 24GB to 32GB or more.

Best Tools to Run AI Locally 2026 - Ollama, LM Studio and More

Running AI on your own computer stopped being a hobbyist project somewhere in the last two years. In 2026 you can download an app, pick a model, and have a capable chatbot running fully offline in about the time it takes to make coffee. Your prompts never leave the machine and there is no monthly bill.

The hard part is no longer "can I do this" but "which tool and which model." Some apps are built for people who never want to see a terminal. Others are engines meant for developers. Below I break down the tools worth installing, the open models worth downloading, and the hardware you actually need, in plain language.

Quick picks - 2026

Best overallOllamaThe default way to download and run open models, with a simple app and a local API every other tool can plug into

Best desktop appLM StudioPolished interface for browsing, downloading and chatting with models, free for home and work use

Best open-source appJanApache 2.0 licensed ChatGPT-style app that runs offline and can also connect to cloud models

Best for documentsAnythingLLMChat with your own files and folders privately, with workspaces and agents built in

Best web interfaceOpen WebUISelf-hosted ChatGPT-style interface for a household or team, on top of Ollama

Best for developersllama.cppThe open-source engine underneath much of the local AI world, with maximum control

Why run AI locally at all?

Three reasons come up again and again. Privacy: client documents, medical notes, unreleased code and personal journals stay on your disk instead of a third-party server. Cost: once you own the hardware, every prompt is free. Control: the model you downloaded today behaves the same way next month, with no silent updates or usage caps. The trade-off is that the very best frontier models still live in the cloud, and local speed depends entirely on your machine. For a lot of everyday work, though, a good open model on a modern laptop is more than enough.

The best tools to run AI locally, reviewed

#1 Overall

Ollama

Free locally + cloud Pro $20/mo

Ollama is the tool most people should start with. Install it on macOS, Windows or Linux, pick a model from its library, and it handles the download, the memory management and the GPU setup for you. There is a simple desktop chat app for casual use, a command line for power users, and a local API that tools like Open WebUI, AnythingLLM, coding assistants and automation scripts connect to out of the box. Running models on your own machine is free and unlimited.

The notable change over the past year is that Ollama now also offers cloud models for when your hardware is not big enough. The free plan includes starter cloud credits, Pro is $20/month and Max is $100/month with larger monthly usage credits and access to bigger models. You never have to use the cloud side, but it is a handy escape hatch for giant models.

Strengths

Easiest setup of any local AI tool
Huge model library, new open models added quickly
Local API that almost every other app supports
Free and unlimited for local use

Limitations

Built-in app is basic compared with LM Studio
Fine-grained tuning of settings needs config files or the CLI

#2 Desktop App

LM Studio

Free + Bionic+ $20/mo

LM Studio is the most polished desktop app for local AI. You search for models inside the app, it tells you which versions will fit on your machine, and you are chatting a minute later. It runs on both llama.cpp and Apple's MLX engine, so Mac users get excellent performance. Version 0.4 added a headless server mode, parallel requests and a refreshed interface, which makes it a credible way to serve models to other apps too. It has been free for work use, not just personal use, since mid-2025, with no commercial license form to fill out.

In July 2026 the team launched Bionic, a separate agent app that can work with files, write code and create documents using local models, with optional open models hosted in LM Studio's own cloud. The core local app stays free; Bionic+ ($20/month) and Pro ($100/month) add cloud usage. If you want a friendly app that still exposes every setting, this is my pick.

Strengths

Best interface for discovering and testing models
Shows which model sizes fit your hardware
MLX support makes it fast on Apple Silicon
Free for personal and commercial use

Limitations

The desktop app itself is not open source
Bionic agent app is a separate download and still early

#3 Open Source

Jan

Free, open source

Jan is the closest thing to a fully open-source ChatGPT you can install on your own computer. It is licensed under Apache 2.0, runs on Windows, macOS and Linux, and downloads models like Llama, Gemma and Qwen straight from Hugging Face. Everything runs offline by default, and the interface will feel familiar to anyone who has used a cloud chatbot.

What makes Jan practical is that it does not force you to pick a side. You can run local models for private work and plug in API keys for OpenAI, Anthropic, Mistral and others when you need a stronger model, all from one window.

Strengths

Fully open source under Apache 2.0
Clean, beginner-friendly chat interface
Mix local and cloud models in one app
Available on Windows, macOS and Linux

Limitations

Fewer advanced model settings than LM Studio
Document chat and memory features are less mature than AnythingLLM

#4 Documents

AnythingLLM

Free desktop + cloud from $50/mo

AnythingLLM is built for one job that most people actually want from local AI: chatting with their own documents. You create workspaces, drop in PDFs, Word files, notes or whole folders, and it builds a private knowledge base that a local model answers from. It also includes agents that can browse, summarize and take simple actions, and it can use Ollama or LM Studio as its model backend.

The desktop app is a free download and runs entirely on your machine. If you want a hosted, multi-user version for a team, the cloud plans start at $50/month for Basic and $99/month for Pro, with an enterprise option for on-premise deployments.

Strengths

Best out-of-the-box document chat (RAG)
Workspaces keep projects separate
Works with Ollama, LM Studio or cloud APIs
Free desktop app, no account needed

Limitations

Answer quality depends heavily on the model you pick
More settings to learn than a plain chat app

#5 Web Interface

Open WebUI

Free self-hosted + Enterprise

Open WebUI gives you a full ChatGPT-style web interface that you host yourself, usually on top of Ollama. Install it with Docker or a single pip command, and everyone on your home network or office can log in from a browser. It supports multiple users with roles, document search, web search, voice, image generation, memory and multi-model conversations.

This is the right tool when you want one powerful machine to serve AI to several people, or when you want the richest feature set without paying for a hosted product. Note that the project now uses its own Open WebUI License, which requires keeping the Open WebUI branding, so check it before rebranding it for a commercial product. An enterprise plan is available through their sales team.

Strengths

Most feature-rich self-hosted chat interface
Multi-user accounts and permissions
Built-in document search and web search
Works with Ollama and any OpenAI-compatible API

Limitations

Needs Docker or Python to install
License requires preserving Open WebUI branding

#6 Developers

llama.cpp

Free, open source (MIT)

llama.cpp is the engine under the hood of much of the local AI world. It is a fast C/C++ inference library that introduced the GGUF model format most local apps now use, and it runs on CPUs, NVIDIA and AMD GPUs and Apple Silicon. It ships a command-line tool and a lightweight server with an OpenAI-compatible API and a basic web chat.

In February 2026 the team behind it, ggml.ai, joined Hugging Face, with the project staying open source and community-led. That should mean faster support for new models straight from the Hugging Face hub. If you want maximum control over quantization, context length and performance tuning, or you are building local AI into your own software, go straight to the source.

Strengths

Maximum control and performance tuning
Runs on almost any hardware, including CPU-only
New models often supported here first
Permissive MIT license

Limitations

Command-line first, not beginner-friendly
You manage model files and settings yourself

#7 Simple Offline

GPT4All

Free

GPT4All from Nomic was one of the first apps to make offline AI easy, and it still works well as a simple, private desktop chatbot on Windows, macOS and Linux. Its LocalDocs feature lets you point it at a folder and ask questions about your files, and it runs on modest hardware, including machines without a dedicated GPU.

The honest caveat: the last release on its GitHub page is version 3.10 from February 2025, so updates have slowed while tools like Ollama and LM Studio ship new features and model support every month. It is still a fine choice for an older computer or a non-technical relative, but for newer models I would start with one of the tools above.

Strengths

Very simple to install and use
LocalDocs for private file Q&A
Runs on modest, CPU-only machines

Limitations

Release pace has slowed noticeably
Newest model families may lag behind other apps

Which open models are worth running in 2026?

The app is only half the decision. The model you download determines how smart your local AI feels. All of these are available through Ollama, LM Studio and Hugging Face, usually in several sizes.

Qwen 3.x (Alibaba)The most versatile open family right now. Qwen3.8-27B is an Apache 2.0 dense model with image and video understanding, and smaller Qwen sizes run on everyday laptops. A strong default for coding, writing and general chat.

Gemma 4 (Google)Released in April 2026 in E2B, E4B, 26B mixture-of-experts and 31B sizes, plus a 12B model added in June. The small "E" models are excellent on laptops and phones, and the 26B MoE runs faster than its size suggests.

DeepSeek V4Open-weight V4-Pro and V4-Flash arrived in April 2026 under an MIT license. They are huge mixture-of-experts models, so treat them as workstation or cloud models rather than laptop ones. Curious how DeepSeek compares to cloud AI? See ChatGPT vs DeepSeek.

Mistral Small 4From Mistral AI, released March 2026 under Apache 2.0. It combines reasoning, vision and coding in a mixture-of-experts design with a small number of active parameters, which helps speed on high-memory machines.

Llama 4 (Meta)Llama 4 Scout and Maverick are still widely supported and well documented, but Meta has since moved its own assistant to a newer model line. Fine if a tutorial or tool expects Llama; otherwise Qwen and Gemma are fresher picks.

What hardware do you actually need?

The one number that matters is memory: system RAM on most PCs, unified memory on Apple Silicon Macs, or VRAM on a dedicated graphics card. Models are measured in billions of parameters (the "B" in 8B or 27B), and local apps usually run a compressed "4-bit" version. A useful rule of thumb is that a 4-bit model needs a bit more than half a gigabyte of memory per billion parameters, plus some headroom for the conversation itself.

8GB of memory: small models in the 1B to 4B range, such as Gemma 4 E2B or E4B. Good for summaries, quick questions and drafting.
16GB: the sweet spot for most laptops. Comfortable with models up to roughly 8B to 14B, which handle everyday writing and coding well.
24GB to 32GB, or a 24GB GPU: opens up the 26B to 32B class, like Qwen3.8-27B or Gemma 4 31B, where local models start to feel genuinely strong.
64GB and up: high-memory Macs and multi-GPU rigs can run large mixture-of-experts models. The biggest releases, like DeepSeek V4, still need workstation or server hardware.

A dedicated GPU or Apple Silicon makes responses much faster, but it is not required to get started. If a model runs too slowly, step down one size.

Which local AI tool is right for you?

Complete beginnerLM Studio or Jan. Both are point-and-click apps that show you which models fit your machine. Start with a small Gemma 4 or Qwen model.

Privacy-focused professionalAnythingLLM on top of Ollama. Keep client files in private workspaces and ask questions without anything leaving your laptop.

Household or small teamOllama on one capable machine with Open WebUI in front of it, so everyone gets a private ChatGPT-style login in their browser.

Developer building with AIOllama for quick local APIs, and llama.cpp when you need full control over performance and model formats.

Older or low-spec computerGPT4All or Jan with a small model. Keep expectations modest and stick to the smallest model sizes.

Needs frontier-level answersUse local models for private work and keep a cloud assistant like Claude or ChatGPT for the hardest tasks. Jan and Ollama let you mix both.