Like many devs, I spent months (okay, years) working with cloud-based AI — mostly OpenAI’s GPT models, sometimes Claude, sometimes Gemini. But recently, I made a switch I never thought I would:
I ditched the cloud and started running my own AI 100% locally. No API keys, no rate limits, no internet needed.
Here’s why — and what actually happened when I tried running serious LLMs on my own hardware.
🧠 The Wake-Up Moment
It started with two things:
- Privacy concerns – I was using AI for personal notes, code, even draft emails. But sending everything to the cloud? Meh.
- API costs – Tokens were adding up. \$50+ a month for chat, just for my own words? 😅
So I asked: Can I do this myself?
🛠️ My Setup
I'm running on:
- MacBook Pro M2 (16GB RAM) for portable tasks
- Desktop with RTX 4070 + 64GB RAM for heavier work
Main tools:
- 🐳 Ollama: 1-command LLM runner
- 🖥️ LM Studio: GUI-based LLM chat tool
- 🧠 Models tested: LLaMA 3 8B, Mistral 7B, Mixtral 8x7B, OpenHermes 2.5
📊 Benchmarks: Real Numbers
| Model | RAM/VRAM Needed | Startup Time | Tokens/sec | Notes |
|---|---|---|---|---|
| LLaMA 3 8B | ~10GB RAM | 4 sec | ~15–20 | Super coherent |
| Mistral 7B | ~7.5GB RAM | 2 sec | ~20–25 | Fastest + smart |
| Mixtral 8x7B | ~13GB RAM | 5–6 sec | ~10–15 | Heavy but accurate |
| OpenHermes | ~6GB RAM | 1.5 sec | ~20–30 | Lightweight chat |
🔐 Privacy Wins
The biggest upside?
Nothing I type leaves my machine.
No usage tracking. No third-party logging. No API outages.
Suddenly, I’m comfortable feeding it code, logs, or sensitive writing without worrying about data exposure.
🧠 What I Use Local AI For Now
- 📝 Personal journaling assistant
- 💬 Chat-style Q&A
- 🧪 Prompt testing for app integrations
- 💻 Local code explanations
- 📑 Embedding + document Q&A (using LM Studio)
🧠 Downsides? Yep.
- You need decent RAM (8GB minimum, 16GB recommended)
- VRAM helps if you use a GPU — Apple M1/M2 do okay, but GPUs shine
- Models still lag behind GPT-4 in deep reasoning
- No built-in search/browsing — but you can build that in yourself 😉
✨ Final Thoughts
I didn’t switch to local AI for fun. I did it because it’s practical, private, and surprisingly powerful.
And now? I’m never going back unless I need GPT-4-level output.
This is my personal experience. Your mileage may vary — especially on older machines. But if you care about privacy, flexibility, or just want to own your AI stack... try going local.
🧠 Own your models. Own your data. It’s more possible now than ever before.