# AI Machine Build for Local AI Work — The Real Deal
**Build Your Own Home AI Box — One Month Performance Review**
Stand up a local rig for web dev, Arduino/Raspberry Pi work, and serious LLM inference — without monthly AI fees.
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## What I Originally Planned (August 2025)
My initial concept was a budget-focused build:
– Basic CPU/RAM setup
– 1TB NVMe for OS
– Focus on smaller 7B models
– Projects: web dev, Arduino/ESP32, 6-channel vibration monitoring
**The Reality Check:** After researching and pricing components, I realized I could build something significantly more powerful for reasonable money, especially with the used GPU market.
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## What I Actually Built (Final Specs)
### Hardware Configuration
**CPU & Motherboard**
– Intel i7-14700K (20 cores: 8P + 12E)
– MSI Z890P motherboard (DDR5 ready, PCIe 5.0)
**Memory & Storage**
– 64GB DDR5 RAM (overkill for most tasks, but handles large context windows)
– Gen5 M.2 NVMe SSD (blazing fast model loading)
**GPU — The Game Changer**
– NVIDIA RTX 3090 Ti (24GB VRAM)
– This was the key upgrade. The 24GB VRAM opens up 30B+ models that simply weren’t possible with smaller cards
**Why These Changes?**
– The i7 + Z890P combo gave me a modern platform with upgrade path
– 64GB RAM eliminates any bottlenecks when working with large datasets
– The 3090 Ti (used market) offered incredible value — 24GB VRAM for the price of new 16GB cards
– Gen5 SSD means model loading times are negligible
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## One Month Reality Check — What Actually Works
### Software Stack
**LM Studio — The Clear Winner**
After testing multiple frontends (Ollama, text-generation-webui, others), **LM Studio** became my daily driver for these reasons:
1. **Clean, functional UI** — No bloat, just works
2. **Server mode** — Other computers on my network can access the AI
3. **Model management** — Easy downloads, switching between models
4. **Stable performance** — Doesn’t crash, doesn’t require tweaking
5. **Cross-platform** — Works identically whether I’m on the main machine or accessing remotely
### What I’m Actually Running
**Primary Model: 30B Parameter Models**
With 24GB VRAM, I comfortably run 30B models with 4-bit quantization:
– **Qwen 2.5 Coder 32B** — My go-to for web development
– **DeepSeek Coder V2 16B** — Backup for faster responses
– Context windows of 16k-32k tokens without performance issues
**Performance Numbers:**
– Model load time: ~5-10 seconds (Gen5 SSD pays off)
– Response generation: 25-40 tokens/second on 30B models
– Can switch models on the fly without system lag
– Network latency over LAN: imperceptible
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## Real-World Use Cases (What I Actually Use It For)
### Web Development — The Primary Workload
**What works exceptionally well:**
– WordPress plugin development and debugging
– PHP/JavaScript code generation and refactoring
– HTML/CSS layout assistance
– SQL query optimization
– API integration code
The 30B Qwen Coder understands context across multiple files, suggests clean code, and rarely hallucinates compared to smaller models.
### Arduino & Raspberry Pi Projects
**Embedded Development:**
– Arduino sketch generation and debugging
– Raspberry Pi Python scripts
– ESP32 firmware for IoT projects
– Explaining register-level operations
– Sensor interfacing code
The model handles both high-level scripting and low-level embedded work without confusion.
### Network Access — The Killer Feature
**LM Studio’s server mode means:**
– My laptop can use the AI without running anything locally
– Other family members can access it for their projects
– Mobile devices can connect for on-the-go queries
– One powerful machine serves the whole household
Set it up once, use it everywhere.
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## Storage Strategy (Simple & Works)
**Current Setup:**
– Gen5 NVMe: OS, applications, and all models
– Models take 5-20GB each depending on size
– 1TB is plenty for 10-15 different models plus OS
**No need for complex mounting or multiple drives yet.** The speed difference of Gen5 means everything loads instantly.
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## The Projects That Actually Happened
### 1. WordPress Development
– Custom theme modifications
– Plugin debugging and feature additions
– WooCommerce integrations
– Database optimization queries
### 2. Pet Supply Business Automation
– Custom design work assisted by AI
– Product description generation
– Inventory management scripts
– Shipping calculator optimization
### 3. Arduino/Raspberry Pi Work
– Home automation scripts
– Sensor data logging projects
– Custom PCB planning assistance
– Debugging embedded C code
### 4. Learning & Experimentation
– Testing different model architectures
– Comparing quantization quality (4-bit vs 5-bit vs 8-bit)
– Prompt engineering techniques
– Local AI capabilities vs cloud services
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## What I Learned After One Month
### What Surprised Me (Positively)
1. **30B models are genuinely good** — The quality jump from 7B to 30B is substantial
2. **Used 3090 Ti was the right call** — 24GB VRAM opens doors that 16GB simply can’t
3. **LM Studio’s simplicity wins** — Fancy features don’t matter if the software crashes
4. **Network mode is essential** — Being able to use AI from any device is game-changing
5. **No cloud dependency feels great** — Privacy, no usage limits, no monthly fees
### What I’d Do Differently
**Nothing major, honestly.** The build exceeded expectations.
Minor notes:
– Could have gone with 32GB RAM instead of 64GB (rarely use more than 24GB)
– A smaller case would have been fine (the hardware doesn’t generate that much heat)
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## Cost Reality Check
While I didn’t track every penny, rough estimates:
– CPU + Motherboard: ~$500-600
– RAM (64GB DDR5): ~$180-200
– Gen5 SSD: ~$120-150
– RTX 3090 Ti (used): ~$800-900
– Case, PSU, misc: ~$200
**Total: ~$1,800-2,000**
**Is it worth it?**
Compare to cloud AI costs:
– ChatGPT Plus: $20/month = $240/year
– Claude Pro: $20/month = $240/year
– API costs for serious usage: $50-200/month
My machine pays for itself in 4-8 months of heavy use, then it’s free forever.
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## The 3-Day Setup Reality
### Day 1 — Getting Running
– Install Ubuntu Desktop 24.04 LTS
– Install NVIDIA drivers (surprisingly easy in 2025)
– Install LM Studio
– Download first model (Qwen 2.5 Coder 7B for testing)
– **Actual time: 2-3 hours** (most of it was downloads)
### Day 2 — Real Work Setup
– Install VS Code + extensions
– Set up Python environment
– Test LM Studio server mode from laptop
– Download 30B model
– **Actual time: 1-2 hours**
### Day 3 — Production Use
– Already using it for real work
– WordPress plugin debugging
– Arduino code generation
– Testing different models and finding favorites
**Total setup time: ~5 hours spread over a weekend.**
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## One Month Later: Would I Do It Again?
**Absolutely yes.**
This machine has:
– Eliminated my cloud AI subscriptions
– Provided better privacy for client work
– Given me complete control over my AI tools
– Taught me how LLMs actually work under the hood
– Paid for itself in saved subscription fees (projected 6-8 months)
The 3090 Ti + 64GB RAM combination handles everything I throw at it, and LM Studio’s server mode means the entire household benefits from one powerful machine.
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## Bottom Line for Anyone Considering This
**Build your own if:**
– You use AI tools daily for work
– You value privacy and data control
– You want to learn how LLMs actually function
– You’re comfortable with basic Linux/PC building
**Stick with cloud if:**
– You only use AI occasionally
– You need absolute latest models immediately
– You don’t want to manage hardware
– Your use case is purely mobile
For me, running local AI has been one of the best tech decisions I’ve made in 2025. The freedom, performance, and cost savings are real.
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**Update Status:** November 2025 — One month of daily use, zero regrets.
*Questions? Hit me up in the comments or check out the original build log for more technical details.*