Privacy is a fundamental human right. And that privacy flows into all aspects of today’s artificial intelligence, too. In a report by Cisco, 64% of people worry they could accidentally share sensitive information when using AI tools.
That’s one of the reasons why offline AI tools are rising in popularity. When the model runs locally, you can write, code, summarize, and create—without uploading everything to the cloud or getting stuck when Wi-Fi drops.
In this list, we’ll cover the best AI tools that work offline, among which is a super app that helps you organize what these tools produce into one cohesive, super secure system.
10 Best AI Tools That Work Offline in 2026
- What Are Offline AI Tools?
- Best Offline AI Tools At a Glance
- What to Look For in the Best AI Tools That Work Offline
- Top 10 Best AI Tools That Work Offline
- 1. ClickUp (Best for keeping tasks, docs, and AI context in one place across offline and online work)
- 3. LM Studio (Best for a polished offline model workbench with performance tuning)
- 4. Ollama (Best for running local LLMs with a simple CLI + local server)
- 5. Jan.ai (Best for an offline, privacy-first ChatGPT-style assistant)
- 6. Llamafile (Best for packaging an LLM into a portable executable file)
- 7. PrivateGPT (Best for offline Q&A over your files with a self-hosted RAG stack)
- 8. Whisper.cpp (Best for fully offline speech-to-text you can embed in apps)
- 9. Text Generation Web UI (Best for users who want a browser-based cockpit for local LLMs)
- 10. Llama.cpp (Best for high-performance local LLM inference across CPU and GPU)
- Turn Offline AI Outputs Into Real Work With ClickUp
- Frequently Asked Questions About Offline AI Tools
Summarize this article for me pleaseWhat Are Offline AI Tools?
Offline AI tools are software applications that run large language models (LLMs) on your local device, without any internet connection needed after you download the model. The model’s data is stored directly on your computer, so all the processing, or inference, happens on your own CPU or GPU.
This on-device processing has several key benefits:
- Complete data privacy: Your sensitive information never leaves your computer
- No recurring fees: Once you have the tool and the model, there are no subscription costs
- Works anywhere: You can use it on a plane, in a remote cabin, or during an internet outage
- Full control: You choose which models to use and how to configure them
Summarize this article for me pleaseBest Offline AI Tools At a Glance
Here’s a quick summary of the best offline AI tools available today.
| Tool name | Key features | Best for | Pricing* |
| ClickUp | Offline Mode for tasks and reminders, ClickUp Brain MAX which includes Talk-to-Text, Enterprise Search across connected apps, Docs and Knowledge Management, Automations, plus Integrations and API | Teams that need offline capture plus online execution, governance, and AI context in one workspace | Free forever; Customization available for enterprises |
| GPT4All | Local chat with open models, LocalDocs for private document Q&A, in-app model discovery and downloads, OpenAI-compatible local API server | Privacy-focused users who want a simple offline desktop chatbot with local docs | Free plan available; Paid plans start at $40/user/month |
| LM Studio | Model discovery and downloads, chat UI plus local RAG, OpenAI compatible server or REST API, presets and performance tuning | Developers and power users who want a polished offline model workbench | Free |
| Ollama | One command model, local REST API with streaming, Modelfiles for reusable configs, and embeddings for RAG pipelines | Developers who want a CLI-first local runtime with a strong API layer | Free plan available; Paid plans start at $20/month |
| Jan.ai | ChatGPT-style offline UI, assistants, and MCP support, extensions for added capabilities, and optional OpenAI-compatible providers | Non-technical users who want a clean offline assistant with customization | Free and open source |
| Llamafile | Single executable model packaging, portable distribution across OS, local server mode with web UI and API, minimal dependency runtime | Users who want a zero-install portable AI file they can run anywhere | Free and open source |
| PrivateGPT | Self-hosted document ingestion and indexing, offline RAG Q&A, context filtering by document, modular LLM, and vector store stack | Teams that need offline Q&A over internal files with a controllable RAG pipeline | Free and open source |
| Whisper.cpp | Local speech to text, quantized models for lower resource use, VAD support, optional FFmpeg handling for more formats | Users who need a fully offline transcription they can embed in apps | Free and open source |
| Text Generation Web UI | Browser-based UI for local models, Jinja2 prompt templates, generation controls, chat branching, and message editing | Users who want maximum customization in a local web interface | Free and open source |
| llama.cpp | High-performance inference engine, wide quantization support, local server with OpenAI-style endpoints, embeddings, and reranking support | Developers building custom offline AI apps or backends | Free and open source |
How we review software at ClickUp
Our editorial team follows a transparent, research-backed, and vendor-neutral process, so you can trust that our recommendations are based on real product value.
Here’s a detailed rundown of how we review software at ClickUp.
Summarize this article for me pleaseWhat to Look For in the Best AI Tools That Work Offline
Evaluating offline AI tools can feel technical and confusing, leading you to choose a tool that might not even run on your computer. The most important thing to consider is what you want to accomplish. A developer building an AI-powered app has very different needs than someone who just wants a private, offline AI chatbot for writing assistance.
Here are the key criteria to evaluate:
- Ease of setup: Does it require technical knowledge and command-line work, or is it a simple one-click installation?
- Model selection: Can you easily choose from a wide variety of models, or are you locked into just a few?
- Privacy guarantees: Does the tool process all data locally, or are there hidden cloud components?
- Hardware requirements: Will it perform well on your current machine, or do you need an upgrade?
- Integration capabilities: Can it connect to your other tools, or is it a completely standalone application?
- Use case fit: Is it designed for general chat, asking questions about your documents, transcribing audio, or generating code?
Summarize this article for me pleaseTop 10 Best AI Tools That Work Offline
Here’s a high-level view of the best AI offline tools 👇
1. ClickUp (Best for keeping tasks, docs, and AI context in one place across offline and online work)
ClickUp, the world’s first Converged AI Workspace, is built on the blocks of ‘offline AI’ that does not stop at generating an answer. That’s because you still need a place where that output becomes a decision, a task, and a next step that does not disappear into files and chats.
And unlike many offline setups that come with lengthy installs and tool stitching, ClickUp gives you a complete execution layer in one place, with AI working on top of the same workspace context.
For starters, you have ClickUp Offline Mode, which automatically turns on and keeps work moving when there’s offline activity. That means all your Tasks, reminders, and notes stay accessible while offline, with the option to add more if needed. Once you reconnect, new tasks and reminders sync back to your Workspace automatically (say goodbye to losing context 👋).
Then there’s ClickUp Brain MAX, the privacy-first desktop AI companion, that can store and search across your entire workspace, connected apps, and even the web.
With Talk-to-Text in the mix, Brain MAX can convert your voice into text hands-free. That includes drafting an email, writing a doc, or capturing a quick update while you are on the run.
Brain MAX also gives you Universal AI, built for chatting with the latest AI models for coding, writing, complex reasoning, and more. That means you get to ask questions to top AI models in one place, including ClickUp Brain plus options like OpenAI, Claude, and Gemini, minus the tool toggling.
Even more, ClickUp Security adds guardrails that offline tools frequently skip. Think encryption, granular permissions, and admin controls like SSO, provisioning, and audit logs, all designed for teams that need enterprise-grade security.
ClickUp AI Knowledge Management helps big time in the ‘offline to online’ handoff. It gives your team a hub to store different resources in Docs and wikis, then uses ClickUp Brain to draw instant answers from across your entire workspace, so the right context is available the moment work resumes.
🎬 Agents in action: Use Super Agents to turn synced work into next steps!
ClickUp Super Agents are AI-powered teammates you can create and customize to run multi-step workflows inside your ClickUp Workspace. You can configure specific triggers, instructions, and tool access to ensure they act within the boundaries you set.
For example, after offline tasks sync back in, a Super Agent can scan the new items, summarize what changed, pull out action steps, draft an update, and route it to the right owner for review.
And because Super Agents are governable, you can control what they can access with permissions and audit what they do. 🔐
ClickUp best features
- Find anything across your tools: Sort and search through your entire workspace, plus connected apps, from one place with ClickUp Enterprise Search
- Build a real knowledge base: Create wikis and documentation with ClickUp Docs using nested pages, templates, AI-assistance, and more
- Keep your stack connected: Sync work with tools like Slack and GitHub without leaving ClickUp with ClickUp Integrations
- Build custom workflows: Use the ClickUp API with personal tokens or OAuth 2.0 to power tailored automations and integrations
- Automate your stack: Trigger actions like assigning owners, updating statuses, or kicking off handoffs based on task changes with ClickUp Automations
ClickUp limitations
- Because of the wide range of features, some users may face a learning curve
ClickUp pricing
free forever
Best for individual users
Free Free
Key Features:
60MB Storage
Unlimited Tasks
Unlimited Free Plan Members
unlimited
Best for small teams
$7 $10
Everything in Free +
Unlimited Storage
Unlimited Folders and Spaces
Unlimited Integrations
business
Best for mid-sized teams
$12 $19
Everything in Unlimited +
Google SSO
Unlimited Message History
Unlimited Mind Maps
enterprise
Best for many large teams
Get a custom demo and see how ClickUp aligns with your goals.
Everything in Business +
White Labeling
Conditional Logic in Forms
Subtasks in Multiple Lists
* Prices when billed annually
The world's most complete work AI, starting at $9 per month
ClickUp Brain is a no Brainer. One AI to manage your work, at a fraction of the cost.
ClickUp ratings and reviews
- G2: 4.7/5 (10,000+ reviews)
- Capterra: 4.6/5 (4,000+ reviews)
What are real-life users saying about ClickUp?
A G2 reviewer says:
2. GPT4All (Best for private, offline AI chat with local LLMs)
Part of Nomic.ai, GPT4All is a desktop app that lets you run open-source large language models directly on your computer, so you can chat with an AI assistant without relying on internet access or cloud API calls. It’s built for people who want a ‘local-first’ setup, where prompts, responses, and files stay on-device.
Its best feature is LocalDocs, which uses a form of retrieval-augmented generation to let you chat with your own documents privately. You can point the app to a folder of PDFs or text files, and it will create a local knowledge base that you can ask questions about.
GPT4All also includes a curated library of popular models like Llama and Mistral, which you can download directly through the app.
GPT4All best features
- Start a local API server (OpenAI-compatible) to use GPT4All models inside other apps and automations
- Search, compare, and download GGUF models from within the app, with sorting options like likes, downloads, and upload date
- Adjust context length, max output length, top-p, top-k, repeat penalty, CPU threads, and even GPU layer offloading (plus Metal support on Apple Silicon)
GPT4All limitations
- Performance is highly dependent on your computer’s hardware
- Indexing large collections of documents can be slow
GPT4All pricing
- Free desktop app
- Business: $40 per user per month (Nomic AI)
- Enterprise: Custom pricing (Nomic AI)
GPT4All ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
What are real-life users saying about GPT4All?
A Reddit user says:
3. LM Studio (Best for a polished offline model workbench with performance tuning)
via LM Studio
LM Studio is a local AI desktop app built around finding, testing, and running open-source models in a UI, without having to rely on a terminal. It’s geared toward experimentation, like picking a model, running it locally, and iterating on prompts and settings with a tighter feedback loop than most CLI-first setups.
It also supports chatting with documents entirely offline (local RAG), where you attach files to a conversation and reference them during responses. That makes it ideal for offline research, study notes, or internal docs workflows where uploads are not an option.
LM Studio also gives you elaborate control over how the AI runs, with options to adjust temperature, context length, and GPU usage.
LM Studio best features
- Use OpenAI compatibility mode or LM Studio’s own REST API, depending on what your app expects
- LM Studio provides both JavaScript and Python SDK options for building local workflows on top of your models
- Save a system prompt + parameters as a preset, then reuse it across chats (presets can also be imported from a file/URL and published to share via the LM Studio Hub)
LM Studio limitations
- LM Studio can freeze the computer while running DeepSeek R1 32B
- Some users flag the app as a data security and privacy concern due to unaudited closed-source code
LM Studio pricing
- Free for home and work use
LM Studio ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
What are real-life users saying about LM Studio?
A Reddit user says:
4. Ollama (Best for running local LLMs with a simple CLI + local server)
Ollama is a local model runner that behaves more like an LLM runtime than a standalone chat app. It’s terminal-first, designed to pull and run open models with quick commands (like, ollama run llama3), then expose them through a local service that other interfaces can sit on top of.
The strength of Ollama lies in its REST API. Once Ollama is running in the background, any application can communicate with it through simple HTTP requests. This helps build AI features into your own software.
Ollama also offers a library of popular models that can be downloaded with one command, and you can create custom model configurations, which are like Dockerfiles for AI.
Ollama best features
- Create reusable, versionable model recipes (base model selection, prompt templates, system messages, parameters, adapters) using a Modelfile
- Run models through endpoints like chat/generate with optional streaming for real-time token output in apps and scripts
- Generate embeddings for semantic search and retrieval pipelines using Ollama’s embeddings capability and recommended embedding models
Ollama limitations
- It has no built-in graphical interface, so non-technical users will need a separate frontend tool
- Ollama’s macOS desktop app may fail to respond while offline, even when models are already downloaded, while the CLI continues to work
Ollama pricing
- Free
- Pro: $20/month
- Max: $100/month
Ollama ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
What are real-life users saying about Ollama?
A Producthunt reviewer says:
🧠 Fun fact: Speech-to-text started as ‘digits only.’ Bell Labs’ AUDREY (1952) recognized the digits 0–9, and one account notes it worked best when spoken by its inventor.
5. Jan.ai (Best for an offline, privacy-first ChatGPT-style assistant)
Jan.ai is an open-source desktop assistant that brings a ChatGPT-like chat experience to macOS, Windows, and Linux, with local-first usage as the default. It runs on-device when you want it to, while conversation history and usage data are stored locally and don’t leave your computer.
It supports running open-source models locally and also allows optional connections to remote providers like OpenAI-compatible APIs, which makes it flexible when offline use is the priority, but cloud access is sometimes needed.
Jan.ai best features
- Create assistants with their own instructions and settings, then switch between them from the Assistants tab instead of rewriting prompts each time
- Connect Jan to MCP tools and data sources using an open standard built for tool-use style workflows
- Install extensions to add new features like web search or a code interpreter. This modular approach lets you start simple and add more power as you go
Jan.ai limitations
- Jan may allow loading a model file that hasn’t fully downloaded yet, which can create confusing or inconsistent behavior until the file is complete
- Turning on the API can interfere with threaded chats, and basic endpoints like ‘get loaded model’ or ‘swap model’ may not work smoothly
Jan.ai pricing
- Open source
Jan.ai ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
What are real-life users saying about Jan.ai?
A Reddit user says:
📮 ClickUp Insight: 88% of our survey respondents use AI for their personal tasks, yet over 50% shy away from using it at work. The three main barriers? Lack of seamless integration, knowledge gaps, or security concerns. But what if AI is built into your workspace and is already secure? ClickUp Brain,
ClickUp’s built-in AI assistant, makes this a reality. It understands prompts in plain language, solving all three AI adoption concerns while connecting your chat, tasks, docs, and knowledge across the workspace. Find answers and insights with a single click!
6. Llamafile (Best for packaging an LLM into a portable executable file)
Llamafile is a Mozilla-led project that bundles a full open-source LLM into one executable file. Instead of installing a runtime, managing dependencies, or wiring up a separate UI, you download one file and run it like an app.
The core idea is distribution. A ‘llamafile’ includes the model weights plus a compiled runtime, built to run across multiple operating systems with minimal setup. It’s especially handy when an offline tool needs to be shared with teammates, students, or customers who won’t troubleshoot installs.
Llamafile best features
- Llamafile combines llama.cpp with Cosmopolitan Libc to support broad binary portability, reducing platform-specific packaging work
- Server mode provides a web GUI plus an OpenAI API compatible completions endpoint, useful for local app development and swapping out cloud calls during testing
- Packaged llamafiles are designed to run on common x86_64 and ARM64 machines, which makes distribution simpler across mixed fleets
Llamafile limitations
- Model selection is limited to what has been packaged as a Llamafile
- File sizes are very large since the entire model is embedded
Llamafile pricing
- Free and open source
Llamafile ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
🧠 Fun fact: The first website is still visitable. CERN literally hosts it at info.cern.ch, calling it the ‘home of the first website.’
7. PrivateGPT (Best for offline Q&A over your files with a self-hosted RAG stack)
PrivateGPT is a production-ready, self-hosted project built for ‘chat with your documents’ workflows. It ingests local files, indexes them, and answers questions by retrieving relevant context from your own content instead of relying on a cloud chatbot’s memory. The project is designed to run fully offline, with the claim that data stays inside your execution environment.
What makes it different from general-purpose offline chat apps is the modular architecture. In other words, you can mix and match the LLM, embedding provider, and vector store based on your hardware and privacy constraints, then run everything behind a local API + UI.
PrivateGPT best features
- Ingest PDFs, DOCX, PPT/PPTX/PPTM, CSV, EPUB, Markdown, JSON, MBOX, IPYNB, images (JPG/PNG/JPEG), and even MP3/MP4 using the built-in ingestion pipeline
- Layer configuration profiles with PGPT_PROFILES (for example, local,cuda) to merge multiple settings files and switch deployments without rewriting your base config
- Filter responses to a specific subset of ingested documents using context_filter when calling contextual completions endpoints
PrivateGPT limitations
- Has higher hardware requirements due to running multiple components simultaneously
- Multi-document ingestion from a single file (for example, one PDF generating one Document per page) can increase indexing volume and complexity when managing large uploads
PrivateGPT pricing
- Open source
PrivateGPT ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
8. Whisper.cpp (Best for fully offline speech-to-text you can embed in apps)
Whisper.cpp is a high-performance C/C++ implementation of OpenAI’s Whisper automatic speech recognition model, built to run locally without heavyweight runtime dependencies. It’s popular for offline transcription pipelines where you want a small, portable binary or a C-style library you can ship inside your own product.
It’s also flexible across environments, with official support spanning desktop, mobile, WebAssembly, Docker, and even Raspberry Pi-class hardware, which makes it a suitable fit for offline tools that need to run in more than one place.
The ‘cpp’ in its name signifies its focus on performance. This implementation is significantly faster and uses less memory than Python-based alternatives, making real-time transcription possible on modern computers without needing a powerful GPU.
Whisper.cpp best features
- Run Voice Activity Detection (VAD) as part of the project to segment speech and reduce noisy, empty-audio transcriptions in streaming-style workflows
- Quantize Whisper models to shrink disk and memory requirements, with multiple quantization options designed for faster local inference
- Build with optional FFmpeg support on Linux to handle more input formats beyond the basic WAV-only path
Whisper.cpp limitations
- Whisper.cpp’s CLI expects 16-bit WAV inputs by default, which adds an extra conversion step when working with MP3, MP4, M4A, or other common formats
- It does not natively label speakers (diarization), which makes it hard to attribute quotes correctly in multi-speaker recordings
Whisper.cpp pricing
- Open source
Whisper.cpp ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
9. Text Generation Web UI (Best for users who want a browser-based cockpit for local LLMs)
Text Generation Web UI (often called ‘oobabooga’) is a Gradio-based web interface for running local models with a heavy emphasis on control and experimentation. It behaves more like a full workbench: multiple model backends, multiple interaction modes, and lots of knobs for generation behavior.
It also leans into ‘writer/dev’ workflows that many offline tools skip, with features like prompt-format automation, notebook-style generation, and conversation branching. For offline setups that still need a web UI, it’s one of the most configurable frontends in this category.
Text Generation Web UI best features
- Automatic prompt formatting using Jinja2 templates, reducing prompt-format mistakes across different model families
- Adjust dozens of settings to fine-tune the AI’s output for your specific needs, whether for creative writing, coding, or roleplay
- Message editing with version navigation and conversation branching, keeping multiple directions of the same chat without starting over
Text Generation Web UI limitations
- The one-click installer requires ~10GB of disk space and downloads PyTorch, which makes setup feel heavy on storage-constrained machines
- The OpenAI-compatible embeddings endpoint uses sentence-transformers/all-mpnet-base-v2 with 768-dimension embeddings, which can break assumptions in pipelines built around OpenAI’s 1536-dimension defaults
Text Generation Web UI pricing
- Open source
Text Generation Web UI ratings and reviews
- G2: Not enough reviews
- Capterra: Not enough reviews
10. Llama.cpp (Best for high-performance local LLM inference across CPU and GPU)
Llama.cpp is a C/C++ inference engine and toolchain for running LLMs locally with minimal dependencies and strong performance across a wide range of hardware. It’s less of a ‘chat app’ and more of a local runtime where you build workflows around, whether that’s a CLI, a local HTTP server, or an embedded library inside your own product.
While not an end-user application itself, Llama.cpp is the engine that powers many of the tools on this list, including GPT4All and LM Studio. It introduced the GGUF model format, which has become the standard for running large models on consumer hardware by efficiently reducing their size.
It also offers bindings for popular programming languages like Python and Rust, and its server mode can provide an OpenAI-compatible API.
Llama.cpp best features
- Launch an OpenAI API compatible local server with a built-in web UI and a /v1/chat/completions endpoint using llama-server
- Run an embedding model (and even a reranking model) from the same server for offline RAG pipelines using /embedding and /reranking
- Quantize models down to 1.5-bit through 8-bit to reduce memory usage and speed up local inference, then accelerate runs with CUDA/Metal plus Vulkan/SYCL backends when available
Llama.cpp limitations
- Llama.cpp does not ship as a polished desktop assistant, which means offline use often involves CLI commands or hosting llama-server, and many users rely on third-party UIs for day-to-day chatting
- Llama.cpp’s OpenAI compatibility is not always 1:1 with every OpenAI client feature, and users report mismatches around structured output parameters like response_format in /v1/chat/completions
Llama.cpp pricing
- Open source
What are real-life users saying about Llama.cpp?
A Sourceforge reviewer says:
Summarize this article for me pleaseTurn Offline AI Outputs Into Real Work With ClickUp
Picking the right offline AI tool is really about matching the tool to the job.
If you’re here, you’re probably optimizing for three things: privacy, no internet dependency, and freedom from being locked into yet another subscription.
The tools on this list deliver on that front in different ways. Meaning, some are better for writing and coding, others for search, notes, or creative work.
But for teams, the bigger challenge is not just running AI locally. It’s turning AI output into real workflows.
With ClickUp’s AI capability layered into your tasks, docs, and knowledge, you can easily surface next steps in the same place where work happens (all with enterprise-grade security).
Try ClickUp for free and see what it looks like when AI and execution finally live together. ✨
Summarize this article for me pleaseFrequently Asked Questions About Offline AI Tools
Can AI tools work completely offline without any internet connection?
Yes, after an initial download of the model files, many AI tools can run entirely on your device with no internet required. This makes them perfect for handling sensitive data or working in locations with poor connectivity.
How do local LLMs differ from cloud-based AI assistants like ChatGPT?
Local LLMs process all data on your personal device, so your information never leaves your machine, while cloud-based AI sends your prompts to remote servers for processing. Local tools are generally free to use after setup, whereas cloud AI often involves subscription fees but may offer more powerful models.
What hardware requirements do offline AI tools need to run smoothly?
Smaller models with 1-3 billion parameters can run on most modern laptops with 8GB of RAM. Larger, more capable models with 7 billion or more parameters perform best with 16GB or more of RAM and a dedicated GPU, with Apple Silicon Macs and NVIDIA GPUs providing significant performance boosts.
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