The 10 Best Local AI Tools in 2026
Running AI models on your own hardware has gone from a hobby to a practical choice. Open models such as Gemma, Qwen, DeepSeek, Mistral and gpt-oss now handle everyday chat, coding and document work, and local tools keep your prompts and files on your machine with no per-token bill.
We picked 10 tools from the 33 local AI products profiled on All About AI and ranked each for the job it does best. For every pick you get what it does, who it suits, key features, pros and cons, current pricing and a bottom line. Details come from vendor pages and project repositories as of late October 2026, and each tool links to a full profile with sources.
Almost every tool here is free. The real cost is hardware: memory, GPU and disk space decide which models you can run and how fast. Also check each model's license, since commercial rights vary from model to model.
10 best local AI tools: TL;DR
- Ollama: Best overall local model runner for developers
- LM Studio: Best desktop app for running local models
- Open WebUI: Best self-hosted ChatGPT-style interface
- Jan: Best open-source offline chat app
- AnythingLLM: Best for private document chat and RAG
- Msty: Best workspace for mixing local and cloud models
- GPT4All: Best for running AI on ordinary laptops
- llama.cpp: Best low-level inference engine
- vLLM: Best for serving models to many users
- PocketPal AI: Best for local AI on your phone
How we chose these tools
We ranked the tools on five criteria:
- Ease of setup: how quickly a new user can download a model and start chatting or serving.
- Model and hardware support: GGUF, MLX and other formats, and support for Apple, NVIDIA, AMD and CPU-only machines.
- Developer access: local APIs, OpenAI-compatible endpoints, SDKs and MCP support.
- Privacy: whether prompts and files stay on your device by default.
- Cost and license: open-source licenses, free tiers and optional paid cloud features.
Comparison table
| Tool | Best for | Price | License | Platforms |
|---|---|---|---|---|
| Ollama | Developers | Free local, cloud from $20/month | MIT | Mac, Windows, Linux, Docker |
| LM Studio | Desktop users | Free, cloud from $20/month | Proprietary app | Mac, Windows, Linux |
| Open WebUI | Self-hosted chat | Free, enterprise license custom | Open WebUI license | Docker, Kubernetes, Python |
| Jan | Offline chat | Free | Apache 2.0 | Mac, Windows, Linux |
| AnythingLLM | Document chat | Free, cloud from $50/month | MIT | Mac, Windows, Linux, Docker |
| Msty | Local plus cloud models | Free, Aurum $149/year | Proprietary | Desktop, web |
| GPT4All | Ordinary laptops | Free | MIT | Mac, Windows, Linux |
| llama.cpp | Inference engine | Free | MIT | Any, CLI and server |
| vLLM | Multi-user serving | Free | Apache 2.0 | Linux servers, GPUs |
| PocketPal AI | Phones | Free, optional purchases | MIT | iOS, Android |
1. Ollama
What it does: Ollama downloads and runs open models on Mac, Windows, Linux and Docker, with a command line, a local REST API and optional Ollama Cloud for larger models.
Best for: Developers and technical users who want the simplest way to run and build on local models.
Ollama launched in 2023 under the MIT license and says it sees more than 9 million monthly installs. You install it, run a model such as Gemma with one command and chat in the terminal, or call the local API on port 11434 from your own apps. Official Python and JavaScript libraries and a Modelfile for custom configurations make it a common backend for other tools on this list.
Its library covers Gemma, Qwen, DeepSeek, Mistral, gpt-oss, GLM, Kimi and more. The CLI can launch coding tools such as Claude Code, Codex and OpenCode against local or cloud models. Ollama Cloud runs models too large for your machine through the same interface.
Speed depends on your memory and GPU, and the main interface is a terminal, though many chat apps connect to it.
Key features
- One-command model runs: download and chat instantly.
- Local REST API: plus Python and JavaScript libraries.
- Large model library: Gemma, Qwen, DeepSeek, gpt-oss and more.
- Ollama Cloud: hosted models with the same interface.
Pros
- Unlimited, free local use.
- Huge community and integrations.
- Prompts stay on your machine for local runs.
Cons
- Performance depends on hardware.
- Cloud use adds credits and limits.
- Less friendly for non-technical users.
Pricing
- Local: free and unlimited.
- Cloud Pro: $20/month with $60 of usage credits.
- Cloud Max: $100/month.
- Team: $500/month.
Bottom line
Ollama is the default way to run open models locally and build apps on top of them. Read the full Ollama review or compare Ollama vs LM Studio.
2. LM Studio
What it does: LM Studio is a desktop app for finding, downloading and chatting with open models, with a local server and developer APIs.
Best for: People who want local AI with a friendly graphical interface.
LM Studio launched in 2023. You search its model catalog, pick a version that fits your hardware, download it and start chatting. It runs models through llama.cpp and Apple's MLX, so it works well on both Windows PCs with GPUs and Apple silicon Macs.
Developers can run a local server with OpenAI-compatible and Anthropic-compatible endpoints, plus REST, Python and TypeScript SDKs. Version 0.4.0 in January 2026 added llmster for headless deployment and parallel requests. The company's separate Bionic app adds agents for coding, research and documents, with optional hosted models.
Large models can exceed consumer memory, and quality varies a lot between downloaded models.
Key features
- Model catalog: search and download in the app.
- llama.cpp and MLX: fast on PCs and Macs.
- Local server: OpenAI- and Anthropic-compatible APIs.
- lms CLI and llmster: scripting and headless use.
Pros
- Easiest graphical local AI app.
- Strong Apple silicon support.
- Free for local use.
Cons
- Big models need lots of memory.
- Cloud features add paid plans.
- Model quality varies.
Pricing
- Free: $0 for local use.
- Bionic+: $20/month for hosted models.
- Pro: $100/month.
Bottom line
LM Studio is the best local AI app for people who prefer a GUI to a terminal. Read the full LM Studio review or compare LM Studio vs Jan.
3. Open WebUI
What it does: Open WebUI is a self-hosted, ChatGPT-style browser interface for Ollama and OpenAI-compatible APIs, with files, knowledge bases, tools and user management.
Best for: Homelabs and teams that want a shared chat interface on their own infrastructure.
Open WebUI started in October 2023 as an Ollama front end and grew into a full browser workspace. You deploy it with Docker, Kubernetes or Python and connect it to Ollama or any OpenAI-compatible endpoint. Users get conversations, file uploads, knowledge bases and tools, and administrators manage accounts, permissions and models.
Optional features add image generation, speech, web search and code execution. Recent releases added encrypted temporary chats, group channels, direct messages and sign-in rate limiting.
You are responsible for securing, updating and backing up the deployment, and branding changes above 50 users need an enterprise license.
Key features
- Multi-backend chat: Ollama and OpenAI-compatible APIs.
- Knowledge bases: document retrieval in chats.
- Admin controls: users, groups and permissions.
- Tools and plugins: web search, code execution and more.
Pros
- Free for internal use.
- Polished, familiar interface.
- Works with local and hosted models.
Cons
- Requires server administration.
- Provider charges are separate.
- Rebranding limits for larger deployments.
Pricing
- Self-hosted: free with Open WebUI branding.
- Enterprise license: custom.
Bottom line
Open WebUI is the best way to give a team a private ChatGPT-style interface. Read the full Open WebUI review or compare Open WebUI vs AnythingLLM.
4. Jan
What it does: Jan is a free, open-source desktop chat app that runs models offline and can also connect to OpenAI, Anthropic, Mistral and Groq with your own keys.
Best for: Privacy-minded users who want an open-source ChatGPT alternative on their desktop.
Jan is built by Menlo Research under the Apache 2.0 license and reports millions of downloads. You install it, pick a model and start chatting with no account. You can download GGUF models from its hub, import your own files and adjust context size, GPU layers and sampling settings.
Jan uses llama.cpp and added native MLX support in February 2026. Later versions added web search, web fetch and MCP support. A local server exposes an OpenAI-compatible API at localhost:1337.
Jan has no enterprise controls, and remote providers follow their own privacy and billing terms.
Key features
- Offline chat: no account required.
- GGUF and MLX models: download or import.
- Remote providers: bring your own API keys.
- Local API and MCP: connect tools and apps.
Pros
- Fully free and open source.
- Clean, simple interface.
- Works offline.
Cons
- Speed depends on hardware.
- No team or admin features.
- Remote models follow provider policies.
Pricing
- Jan Desktop: free.
Bottom line
Jan is the best open-source desktop app for private, offline AI chat. Read the full Jan review or compare Jan vs GPT4All.
5. AnythingLLM
What it does: AnythingLLM is an open-source workspace for chatting with documents, running agents and connecting local or hosted models, on desktop or a self-hosted server.
Best for: Individuals and teams who want private document Q&A without sending files to a cloud chatbot.
AnythingLLM is built by Mintplex Labs under the MIT license. You install the desktop app or deploy with Docker, choose a local model such as one served by Ollama or a hosted provider, create a workspace and upload documents. Answers cite the passages they drew from.
Workspaces keep document collections and instructions separate. Agents can use tools, browse the web and run tasks, and administrators manage users, permissions and a developer API. It supports many embedding models, vector databases and rerankers.
Flexibility means more setup, and cloud plans require you to bring your own model API key.
Key features
- Document chat with citations: built-in RAG.
- Workspaces: separate collections and instructions.
- Agents: tools, web browsing and scheduled tasks.
- Multi-user and API: permissions and developer access.
Pros
- Free desktop and self-hosted versions.
- Works with local or hosted models.
- Strong document features.
Cons
- Performance varies with your setup.
- Cloud plans need your own model key.
- Cloud instances lack GPUs for embeddings.
Pricing
- Desktop and self-hosted: free.
- Cloud Basic: $50/month.
- Cloud Pro: $99/month.
- Enterprise: custom.
Bottom line
AnythingLLM is the best local tool for chatting with your own documents. Read the full AnythingLLM review.
6. Msty
What it does: Msty Studio is a desktop and web workspace for local and hosted models, with side-by-side chats, document Knowledge Stacks, agents and personas.
Best for: Power users who switch between local and cloud models and want one polished app.
Msty began as a desktop client and relaunched as Msty Studio in 2025. It runs local models through Ollama, llama.cpp and MLX and connects to hosted providers with your keys. Split Chats show answers from several models side by side, which helps you compare a local model with a cloud one.
Knowledge Stacks add document retrieval, Agent Mode handles multi-step tool use, and Crew Conversations put several personas in one thread. Prompt, Persona, Skills and Media Studios save reusable assets.
Studio Web needs the desktop app or Sidecar to reach local models, and the older Msty App is being phased out.
Key features
- Split Chats: compare models side by side.
- Knowledge Stacks: document retrieval.
- Agent Mode and Crew Conversations: multi-step and multi-persona work.
- Local engines: Ollama, llama.cpp and MLX.
Pros
- Polished app for local and cloud models.
- Free desktop tier.
- Lifetime license option.
Cons
- Web version needs desktop for local models.
- Legacy app being retired.
- No published usage quotas.
Pricing
- Free: $0 for core desktop features.
- Aurum: $149/user/year.
- Aurum Lifetime: $349/user one-time.
- Teams: custom, five-seat minimum.
Bottom line
Msty is the best workspace for people who mix local and cloud models daily. Read the full Msty review or compare Msty vs LM Studio.
7. GPT4All
What it does: GPT4All is Nomic AI's free desktop app and Python library for running quantized models locally, with LocalDocs for private document chat.
Best for: Beginners running AI on everyday laptops without a strong GPU.
GPT4All launched in 2023 and was one of the first apps to make local models easy on ordinary computers. Its documentation says some models run with 4 to 8 GB of RAM. You install the app, pick a model from the gallery, download it and start chatting. LocalDocs lets you ask questions about a folder of files without sending them anywhere.
Developers can install the Python package and run a local OpenAI-compatible server. The MIT-licensed project has more than 77,000 GitHub stars.
The latest listed release, v3.10.0, dates from early 2025, so updates have slowed compared with other tools here, and CPU inference can be slow with long contexts.
Key features
- LocalDocs: private document search and chat.
- Model gallery: quantized models for modest hardware.
- Python SDK: local inference in code.
- Local API server: OpenAI-compatible endpoint.
Pros
- Runs on modest laptops.
- Free and MIT licensed.
- No account needed.
Cons
- Slower update pace.
- CPU inference can be slow.
- Models use several GB of disk each.
Pricing
- Desktop and SDK: free.
Bottom line
GPT4All is a good first local AI app for ordinary laptops, though newer apps move faster. Read the full GPT4All review.
8. llama.cpp
What it does: llama.cpp is the open-source C and C++ inference engine behind many local AI apps, with quantization, broad hardware support and an OpenAI-compatible server.
Best for: Developers who want maximum control over local inference.
llama.cpp was created in March 2023 and is MIT licensed. In February 2026 its founding team joined Hugging Face, while the project stays fully open. Ollama, LM Studio, Jan, GPT4All and PocketPal all build on it. You can build from source, use a prebuilt binary or Docker, download a GGUF model and launch the CLI or llama-server.
It supports quantization from 1.5-bit to 8-bit and runs on CPUs, Apple Metal, NVIDIA CUDA, AMD HIP, Vulkan and SYCL. Hybrid CPU and GPU inference lets you run models larger than your VRAM. Tools for conversion, benchmarking, embeddings and grammar-constrained output are included.
It is infrastructure, not a consumer app, so setup needs technical skill.
Key features
- Quantization: 1.5-bit to 8-bit.
- Hardware backends: CPU, Metal, CUDA, HIP, Vulkan and SYCL.
- llama-server: OpenAI-compatible API.
- Hugging Face download: fetch models with one flag.
Pros
- Runs almost anywhere.
- Fastest path to new model support.
- Free and MIT licensed.
Cons
- Command-line setup.
- No bundled models.
- Tuning needs technical judgment.
Pricing
- Open source: free.
Bottom line
llama.cpp is the engine to use when you want full control over local inference. Read the full llama.cpp review or compare llama.cpp vs vLLM.
9. vLLM
What it does: vLLM is an open-source, high-throughput inference engine for serving open models to many users on GPUs, with an OpenAI-compatible server.
Best for: Teams self-hosting models as an internal API for apps or many users.
vLLM came out of UC Berkeley's Sky Computing Lab and is Apache 2.0 licensed, with more than 2,000 contributors. You install the Python package, pick a Hugging Face model and run vllm serve. PagedAttention manages GPU memory efficiently, and continuous batching combines many requests for higher throughput.
It supports tensor, pipeline, data and expert parallelism across several GPUs, many quantization formats, structured outputs and tool calling, and both OpenAI and Anthropic API formats. The V1 architecture, introduced in 2025, redesigned the scheduler and added prefix caching.
vLLM needs GPU drivers, memory planning and operations work, and has no bundled support SLA.
Key features
- PagedAttention: efficient GPU memory use.
- Continuous batching: high concurrent throughput.
- Multi-GPU parallelism: for larger models.
- OpenAI-compatible server: easy migration from hosted APIs.
Pros
- Built for production serving.
- Large, active community.
- Free and Apache 2.0 licensed.
Cons
- Needs GPUs and ops skills.
- No hosted service or SLA.
- Feature coverage varies by model.
Pricing
- Open source: free.
Bottom line
vLLM is the best choice for serving open models to many users. Read the full vLLM review.
10. PocketPal AI
What it does: PocketPal AI is a free, open-source app that runs language models locally on iPhone, iPad and Android, offline after download.
Best for: Anyone who wants private AI chat on their phone.
PocketPal AI launched in 2024 under the MIT license. You open its model browser, download a GGUF model that fits your device, load it and chat with no account. Hugging Face integration lets you search and download models, and you can create Pals that pair a model with a system prompt and personality.
It runs on llama.cpp through llama.rn, with Metal acceleration on iOS, GPU acceleration on Android and Qualcomm NPU support where available. Local text-to-speech, calculator and date tools and a benchmark mode are included.
Phone memory and heat limit model size and speed, and some personas and features are paid extras.
Key features
- On-device chat: offline after model download.
- Hugging Face model browser: GGUF downloads.
- Pals: custom assistants.
- Local text-to-speech: voice output on device.
Pros
- Free and open source.
- Private by default.
- Works on iOS and Android.
Cons
- Limited by phone memory and heat.
- Model licenses are your responsibility.
- Some extras cost money.
Pricing
- App: free.
- Optional purchases: $0.99 to $44.99.
Bottom line
PocketPal AI is the best way to carry a private AI model on your phone. Read the full PocketPal AI review.
How to choose the right local AI tool
Start with how you want to use local models:
- Chat on your own computer with a GUI: LM Studio, Jan or Msty.
- Build apps on local models: Ollama.
- A shared chat interface for a team: Open WebUI connected to Ollama.
- Ask questions about private documents: AnythingLLM, or GPT4All LocalDocs on a laptop.
- Maximum control or unusual hardware: llama.cpp.
- Serving many users from GPUs: vLLM.
- AI on your phone: PocketPal AI.
Match model size to your memory. As a rough guide, small 3B to 8B models run on most modern laptops, while 30B and larger models need a lot of RAM or a strong GPU. Start small, then move up if quality is not enough.
FAQs
What is the best way to run AI locally?
For most developers, Ollama is the easiest starting point. If you prefer a graphical app, LM Studio or Jan let you download and chat with models in a few clicks.
Is local AI private?
When you run a model locally, prompts and responses stay on your device. Privacy changes if you connect a cloud provider, use cloud features such as Ollama Cloud or LM Studio's Bionic+, or install plugins that call outside services.
What hardware do I need to run AI locally?
Smaller quantized models run on many laptops with 8 to 16 GB of memory, and GPT4All lists some models running in 4 to 8 GB. Larger models need more RAM or a GPU with lots of VRAM. Apple silicon Macs work well with MLX-based tools such as LM Studio and Jan.
Are local AI tools free?
Yes, all 10 tools have free local use. Paid options cover cloud models (Ollama, LM Studio), hosted instances (AnythingLLM), advanced features (Msty) or enterprise licenses (Open WebUI).
Are local models as good as ChatGPT or Claude?
The best open models are strong for many everyday tasks, but the top hosted models still lead on the hardest reasoning and coding work. Local models trade some capability for privacy, offline use and no per-token cost.