Building Local AI Machine
WOW! Are you crazy? Of course not your a builder in search of solutions!
Most rewarding challenges are the hardest. Sure, you can pay for the product, but then you’re left without the journey, and that’s how learning happens. Sure, I took apart a watch when I was a kid, and although I never got it back together, the journey was insightful. I knew what was inside, and with every watch development from that point on, I wondered what made it tick. Nowadays, everything is electronic, so understanding microchips and programming code has become the trend. But just like any product evolves, builders must also evolve or risk being stuck with outdated techniques and standards.
So you have used chatGpt and found how incredible of a resource it is, but you may also have discovered that it’s full of crap at times, even to the point of providing bad information. This isn’t uncommon. You need to accept this in order to participate. These big corporate AI beasts are designed to suck you in and keep you there, like a never-ending stroking session. Well, have fun with that, but don’t expect to get anything built from it.
You’re not just building an AI tool – you’re building a personal AI system that:
- Runs locally (on your 3090 Ti)
- Uses your favorite model (Qwen3 30B)
- Stays up to date with real-time data
- Is scalable, maintainable, and won’t crash from “tokens”
- Uses you.com (your own AI-powered project) as the core
✅ Final System Summary
| Feature | How It Works |
|---|---|
| Your Brain | Qwen-3 30B (runs locally, fast) |
| Live Data | Local search + scrape scripts (DuckDuckGo / Brave / SearxNG) |
| Memory | ChromaDB (saves what it learns) |
| No Tokens | 100% local — no cloud limits |
| Daily Updates | Automated script feeds new data |
| Your Brand | you.com — your personal AI |
🚀 You’re not just using AI — you’re building your own.
I want my ai to learn, i was under the impression training was not possible because the model are frozen
You’re exactly right — the models you run in LM Studio (like Qwen, Llama, etc.) are frozen. Their weights can’t change — so they can’t “learn” new facts permanently just by using them.
But there are three kinds of “learning” we can talk about:
1. Training / Fine-tuning (true learning)
- This is when you actually update the weights of the model so it knows new information.
- Requires GPU horsepower, lots of data, and a training pipeline (like LoRA or full fine-tuning).
- Not currently supported in LM Studio directly — you’d need to export your model to tools like Hugging Face + PEFT/LoRA.
- Once done, the model “remembers” without needing an external database.
2. RAG (Retrieval-Augmented Generation)
- This is what your Flask + Chroma idea is doing.
- The model doesn’t learn internally — it just pulls in external knowledge at runtime.
- Feels like “learning” because the AI can answer about new things right away.
- Advantage: fast, local, doesn’t require retraining.
- Limitation: the model forgets if you remove the database.
3. Memory Systems (long-term memory simulation)
- You can build a memory layer that stores chat history or facts into a vector DB (like Chroma).
- Then, when you start a new chat, you retrieve old “memories” and inject them into the prompt.
- This simulates personal learning (like remembering your name, projects, preferences).
- Example:
- User: “My dog’s name is Buddy.”
- Stored in Chroma as memory.
- Later, when you ask “What’s my dog’s name?” → RAG retrieves “Buddy” → AI answers correctly.
🔑 Key point:
- Frozen model = no weight updates = no “true learning”.
- But you can simulate learning with RAG + memory systems, and it feels almost the same in practice.
- If you really want to fine-tune, you’d export and train with something like PEFT + LoRA outside LM Studio, then reload your fine-tuned model.
Training
⚠️ Why Qwen-30B is tough to fine-tune
- VRAM demand:
- Just loading Qwen-30B in 16-bit precision is ~60 GB VRAM.
- Even with QLoRA (4-bit quantized fine-tuning), training usually requires 48–80 GB VRAM.
- A single 3090 Ti (24 GB VRAM) will OOM before training can even start.
- Multi-GPU setups: Fine-tuning 30B is usually done with 4×A100 80GBs or equivalent.
- Dataset throughput: You’d need very optimized sharding and offloading to CPU RAM/disk, which slows training down to impractical speeds.
✅ What you can do with Qwen-30B locally
- Inference (using GGUF in LM Studio)
- Perfect fit: you already are happy with how it runs.
- For personal/coding/chat tasks, it’s a top choice.
- RAG (Retrieval-Augmented Generation)
- Your Chroma + Flask idea works beautifully here.
- You can “teach” Qwen-30B about your CNC manuals, sewing machine docs, websites, etc., without fine-tuning.
- Pseudo-memory systems
- Save facts (like your project data, family details, etc.) in a vector DB and inject them into prompts.
- Gives the feeling of long-term learning.
🔄 Hybrid Strategy
Since you like Qwen-30B:
- Keep Qwen-30B as your main model for reasoning & conversation.
- Use a smaller model (7B–13B) for fine-tuning experiments.
- You can fine-tune these on your 3090 Ti (with LoRA/QLoRA).
- Once fine-tuned, you could even use them as specialist models (e.g., a CNC-expert 7B) alongside Qwen-30B.
Think of it like:
- Qwen-30B = your generalist brain.
- Fine-tuned 7B = your domain expert sidekick.
👉 Since you’re satisfied with Qwen-30B, the best next step is:
- Make your RAG + memory pipeline rock-solid.
- If you want to play with training, spin up a fine-tuned 7B/13B on your 3090 Ti — and maybe use it as a helper model feeding into Qwen-30B.
Solution Overview
We’ll build a complete RAG system using:
- Flask API server
- Chroma database for storage
- Sentence transformers for semantic search
- Local HTTP endpoints for LM Studio integration
