AI Studio 2025: Which hardware is really worth it - from the Mac Studio to the RTX 3090

Hardware 2025 for AI studio

Anyone working with AI today is almost automatically pushed into the cloud: OpenAI, Microsoft, Google, any web UIs, tokens, limits, terms and conditions. This seems modern - but is essentially a return to dependency: others determine which models you can use, how often, with which filters and at what cost. I'm deliberately going the other way: I'm currently building my own little AI studio at home. With my own hardware, my own models and my own workflows.

My goal is clear: local text AI, local image AI, learning my own models (LoRA, fine-tuning) and all of this in such a way that I, as a freelancer and later also an SME customer, am not dependent on the daily whims of some cloud provider. You could say it's a return to an old attitude that used to be quite normal: „You do important things yourself“. Only this time, it's not about your own workbench, but about computing power and data sovereignty.

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RAG with Ollama and Qdrant as a universal search engine for own data

Extend local AI with databases using RAG, Ollama and Qdrant

In an increasingly confusing world of information, it is becoming more and more important to make your own databases searchable in a targeted manner - not via classic full-text searches, but through semantically relevant answers. This is exactly where the principle of the RAG database comes into play - an AI-supported search solution consisting of two central components:

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Local AI on the Mac: How to install a language model with Ollama

Local AI on the Mac has long been practical - especially on Apple-Silicon computers (M series). With Ollama you get a lean runtime environment for many open source language models (e.g. Llama 3.1/3.2, Mistral, Gemma, Qwen). The current Ollama version now also comes with a user-friendly app that allows you to set up a local language model on your Mac at the click of a mouse. In this article you will find a pragmatic guide from installation to the first prompt - with practical tips on where things traditionally go wrong.

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