AI & Local AI

What are AI tools, and what does local AI mean?

AI tools is an umbrella term for software that turns machine learning into something usable for concrete tasks: writing and summarizing text, transcribing speech, processing images, completing code. The well-known services like ChatGPT run in the cloud, so every request travels to someone else's servers. Local AI turns that around: the models run on your own machine. This is made possible by open models like Llama or Mistral, which work in compressed form even on ordinary consumer hardware, and by apps that handle downloading, managing, and operating these models. This category collects both: LLM tools for local use, and applications that build AI features into classic tasks like video editing or PDF work. Developers searching for AI tools often mean code assistants; those now run locally too, with trade-offs against the big cloud services.

Typical use cases

Writing and research with a local language model: An LLM client like Faraday.dev downloads open models and serves them as a chat, fully offline. That suits everything that has no business on third-party servers: contract drafts, HR topics, unpublished texts. Conversations and models stay entirely inside the app on your device.

Transcribing audio: Since OpenAI released the Whisper speech recognition model, transcription at good quality runs locally. Interviews, meetings, and dictation stay on your own device, which makes the difference for confidential conversations. Tools for this exist as desktop apps with an editor and as command line tools for batch processing.

Training your own models without code: Liner.ai trains classification models for images, text, or audio from your own example data, no programming required. That solves specialized tasks no ready-made model covers, such as sorting your own product photos.

AI inside creative software: Established tools build AI features right in. DaVinci Resolve uses them for automatic masking and audio cleanup in video editing, and PDFGear answers questions about PDF documents in a chat. Here AI is assistance inside the tool, not a product of its own.

What matters when choosing

Privacy and data sovereignty: Prompts often reveal more about you than you would like: customer data, strategy, source code. With local AI none of it leaves your machine, which settles the GDPR question for many use cases from the start. With cloud services you have to clarify whether inputs are used for training and where the servers are located. For companies, data processing agreements come on top: a local model removes the contract with the AI vendor entirely.

Hardware requirements: Local language models mainly need memory. Small models run from about 8 GB of RAM, things get smoother with 16 GB or a graphics card with enough VRAM; Macs with Apple Silicon do well thanks to unified memory. Before picking a tool, check which model sizes your hardware can realistically handle.

Model ecosystem: Tools that support open model formats like GGUF let you swap models freely and try new releases immediately. Apps with hardwired models are more convenient but tie you to the vendor's update policy.

Cost model: Cloud AI bills per request or by subscription, and costs grow with usage. Local AI costs hardware once, then electricity. With regular, heavy use the math quickly tips toward the local option; for occasional requests the cloud stays cheaper.

Local AI or cloud service: where the limits are

The big cloud models remain ahead of locally runnable ones in quality and breadth of knowledge, and no amount of enthusiasm makes that gap disappear. It does shrink year by year: small open models now handle tasks that needed a data center two years ago. For demanding tasks like long analyses or complex code, the cloud delivers noticeably better results. Local models play their strengths elsewhere: with sensitive data, with high-volume tasks, offline, and wherever predictable costs matter. In practice a division of labor works well: routine tasks, drafts, and anything confidential stay local, while the hard edge cases go to the cloud. If you want to keep both paths open, pick tools that can connect API providers alongside local models.

Frequently asked questions

Which AI tools run locally without internet?

Three families run locally above all: LLM clients that execute open language models on your machine, transcription tools based on Whisper, and training tools for your own classification models. After the one-time model download they work without a connection; you only need internet access for updates and new models.

What hardware does local AI need?

A current computer with 8 to 16 GB of RAM is enough to start; small, compressed language models run at usable speed on that. More memory allows larger and therefore more capable models. A dedicated graphics card speeds things up considerably, and Macs with Apple Silicon benefit from memory shared between CPU and GPU.

Are local AI tools free?

Many are: a large part of the scene is open source, and the open models themselves cost nothing. What you pay for is the hardware everything runs on, and the electricity. Some desktop apps charge license fees for convenience features; the basic principle of local models remains untouched by that.

The selection above is deliberately curated and still compact; it grows with the scene. If data sovereignty is your main motive, start with the tools that run models entirely on your machine. The detail pages classify each tool accordingly.