🎮 From Gaming Rig to AI Brain: My Accidental Deep Dive into Local AI with an RTX 3090

1. Introduction: The Unplayed Games #

I’ve wanted to dabble with local AI for a long time, mainly because of the possibilities it unlocks. Local AI allows me to use all of my data—financial records, personal messages, location history, even transcribing everything I say—without the privacy concerns of sending it to a cloud provider. This level of deep, personal data analysis is only something I’d be comfortable with on my own hardware.

However, I couldn’t justify buying a powerful GPU just for an imagined AI project that I might never really start. Then it hit me: I could also use it for gaming (duh). Because I really enjoyed The Last of Us series, I thought, “Okay, why not play The Last of Us on PC?” This was the push I needed. So, a couple of weeks ago, I convinced myself to buy someone’s old gaming machine. After many deep research sessions with ChatGPT about the best bang-for-the-buck consumer-grade GPU, I landed on an NVIDIA 3090. I found an (old) beast of a machine, and my initial idea was set.

Fast forward four weeks. I haven’t played a single minute, but my machine is now indexing 200 GB of email archives and facing a serious performance bottleneck I’ll get to later. That “gaming” machine has been humming away, but not rendering virtual worlds. It’s been training, transcribing, and thinking.

I’m writing this post simply because I am so enthusiastic about local AI. I’ve found that local models are surprisingly powerful for specific use cases. Of course, they aren’t going to replace Gemini or Claude for coding, but for many other applications, they work incredibly well. Lately, I’ve been so obsessed with this that I’m sleeping significantly less and spending all my free hours working on it.

2. The Rocky Road to a Working Setup #

Before I could even get to the fun AI stuff, I had to wrestle with the machine itself. I started with Pop!_OS, which is supposed to be a great out of the box solution that “just works”. However, it’s been a long time since I used Linux on the desktop; my experience is almost exclusively with servers. I quickly installed some of the wrong NVIDIA drivers and ended up debugging stuff in Grub. I got frustrated with the different commands I had to run to set up everything and realized that reproducing this system was going to be a massive pain. Since I’ve grown to love Nix on my Mac with Nix-Darwin, I decided to switch the whole system to NixOS. This has been an amazing decision. Even though there are many pain points with Nix, I think it is well worth it. For those interested, you can see the complete setup, including all the local AI services, in my NixOS configuration on GitHub.

3. The First Victory: agent-cli 🐍 #

With a stable system, I quickly got started with a few scripts. These soon evolved into my first real project of this adventure: agent-cli.

agent-cli is a collection of local-first, AI-powered command-line agents that run entirely on your machine. The core philosophy is privacy; your data never leaves your computer. It’s designed for seamless integration with system-wide hotkeys. I’m now using it constantly.

Instead of typing, I now dictate almost everything. A quick hotkey, and agent-cli transcribe turns my speech into text. Another hotkey, and agent-cli autocorrect cleans it up using a local LLM running on Ollama. It has genuinely streamlined my workflow. For example, I can pipe the tools together, like correcting text from my clipboard and then having it spoken aloud: agent-cli autocorrect | agent-cli speak.

The toolkit includes:

• autocorrect: Fixes grammar and spelling.
• transcribe: Uses a local Whisper model for speech-to-text.
• speak: Converts text to speech with a local TTS engine.
• voice-edit: A voice-powered clipboard assistant.
• assistant: A hands-free voice assistant using a wake word.
• chat: A conversational AI with tool-calling capabilities.

It’s been a fantastic success in my personal workflow, and it’s all open-source for you to try. Contributions and ideas welcome!

Once I trusted my toolchain, the obvious next step was to feed all my data into it.

4. The Grand Ambition: AIBrain 🧠 #

My second package, AIBrain, is a much larger endeavor. The vision is to create a “life-OS”—a private, local AI that processes all my personal data (emails, calendar, files, photos, messages, browser history, location data) to generate summaries and answer questions, eventually allowing for queries like:

aibrain ask "When did I get into landscape photography?"
aibrain ask "How many countries did I visit between 2015-2025?"

This project led me to explore the landscape of agentic frameworks. I started with CrewAI, but I quickly found that despite the hype and funding, it had some painful problems (specifically this issue where things just fail without any helpful error messages). I decided to switch to LangGraph, which felt more robust. I also chose to not use PydanticAI (which I used in agent-cli) because its Ollama support is just a wrapper around the OpenAI-compatible API, which doesn’t expose all the useful options (I even opened a PR in Ollama to make its OpenAI compatible API more feature-complete, but it has not been merged yet).

The core infrastructure of AIBrain is ready. It can index emails, calendar events, and files in various formats (PDF, Excel, PowerPoint, images). However, I’ve hit a significant challenge.

5. Conclusion: The Journey Continues #

This past month has been a whirlwind. I set out to play games and ended up with two open-source AI packages and a much deeper appreciation for the power sitting in a consumer-grade GPU. It’s incredible what you can achieve with local hardware today.

agent-cli is a finished, polished tool that I use daily. AIBrain is a much bigger dream, and I’m still figuring out how to solve the performance puzzle to make it truly practical. Next up: smarter chunking with LangGraph, vector-DB swap-outs, and GPU scheduling tricks.

This experience has reinforced my passion for local-first software and open-source development. The future of personal, private AI is being built right now, not just in large corporate labs, but in the homes of hobbyists tinkering with their gaming rigs.

⭐ If this resonates, star the repos & drop issues/questions.

And who knows, maybe I’ll even find the time to play ‘The Last of Us’.