My Local AI Stack
My Local AI Stack
Introduction
A few weeks ago, I set up a local AI coding assistant on my laptop that runs entirely offline. No cloud services, no monthly subscriptions, no data leaving my machine.
And it actually works really well.
In this article, I'll walk you through what I built, the alternative options available, why it matters, and how you can try it too. Whether you're a developer, a student, or just curious about AI - there's something here for you.
The Stack
Here's my current setup:
| Component | What I Use | Other Options |
|---|---|---|
| AI Agent | OpenWork | OpenCode CLI, Claude Code, Cursor, GitHub Copilot |
| LLM Runtime | Ollama | LM Studio, llama.cpp, text-generation-webui, GPT4All |
| AI Model | Llama 3.2 (3B) | Phi 3.5, Mistral, Qwen, Codellama, Gemma |
| Platform | Linux | macOS, Windows, WSL |
My Configuration
- RAM: 8GB (plenty for 3B models!)
- Storage: ~3GB for the model
- Time to setup: About 30 minutes
Component Options Explained
AI Agents / Coding Assistants
| Tool | Type | Best For |
|---|---|---|
| OpenWork (what I use) | Full workspace agent | Complete development workflow |
| OpenCode CLI | CLI-based agent | Terminal-first developers |
| Claude Code | Autonomous CLI agent | Complex multi-step tasks |
| Cursor | IDE-integrated AI | IDE users wanting AI |
| GitHub Copilot | IDE plugin | Inline code suggestions |
LLM Runtimes
| Runtime | Platform | Best For |
|---|---|---|
| Ollama (what I use) | All platforms | Ease of use, great model library |
| LM Studio | macOS/Windows | GUI, model management |
| llama.cpp | All platforms | Maximum performance, no GPU needed |
| text-generation-webui | All platforms | Web UI, extensive features |
| GPT4All | All platforms | Privacy-focused, local-only |
AI Models
| Model | Size | Strengths | RAM Needed |
|---|---|---|---|
| Llama 3.2 3B (what I use) | 3B | Balanced performance | 6-8GB |
| Llama 3.2 1B | 1B | Lightweight, fast | 4GB |
| Phi 3.5 | 3.8B | Microsoft's efficient model | 6-8GB |
| Mistral 7B | 7B | Great reasoning | 12-16GB |
| Codellama 7B | 7B | Code-specialized | 12-16GB |
| Qwen 2.5 | 7B | Multilingual | 12-16GB |
| Gemma 2B | 2B | Google's lightweight option | 4GB |
What Can It Do?
My local AI assistant handles:
Development Tasks
- Write and edit code files
- Debug issues and explain errors
- Refactor and improve code
- Search through codebases
- Run terminal commands
General Tasks
- Answer technical questions
- Explain programming concepts
- Help with shell commands
- Do web research (when online)
It's not as smart as the big cloud AI systems, but for everyday development tasks, it's incredibly useful.
The Architecture: Why This Matters
Here's the cool part - everything runs locally:
┌─────────────────────────────────────────────────┐
│ My Laptop │
│ ┌─────────────┐ ┌─────────────────────┐ │
│ │ OpenWork │───▶│ Ollama │ │
│ │ (AI Agent) │ │ (Local LLM Runtime) │ │
│ └─────────────┘ └─────────────────────┘ │
│ │ │
│ ┌──────▼──────┐ │
│ │ Llama 3.2 │ │
│ │ (3B) │ │
│ └─────────────┘ │
└─────────────────────────────────────────────────┘
100% Offline Capable! 🔒No API calls. No data leaving my machine. No monthly bill.
Key Advantages
1. Privacy First
Your code, your files, your data - all stay on your machine. For developers working on proprietary projects, this is huge.
2. No Ongoing Costs
Once set up, it's free. No per-token billing, no subscription fees.
3. Works Offline
Perfect for travel, remote work, or spotty internet.
4. Zero Latency
No network round-trips. Responses are instant.
5. Complete Control
You own the stack. You decide when to update and what models to use.
Security Considerations 🔐
Running local AI has its own security profile:
| Aspect | Local AI | Cloud AI |
|---|---|---|
| Data transmission | None | All data sent to cloud |
| API keys | Not needed | Required |
| Updates | Manual | Automatic |
| Network exposure | Minimal | Standard web |
Best Practices:
- Keep your system software updated
- Use strong passwords on your machine
- Be mindful of what files you share externally
- Use a VPN for additional privacy when researching online
Pricing Breakdown 💰
One of the biggest advantages? The cost.
| Item | Local AI Stack | Claude Pro |
|---|---|---|
| Monthly | $0 | $20-200/month |
| Year 1 | $0 | $240-2,400/year |
| Year 2 | $0 | $240-2,400/year |
| Year 3 | $0 | $240-2,400/year |
My total investment: Just my time (~30 min to set up).
Optional upgrades (not required):
- Extra RAM (16GB): ~$50-100
- SSD upgrade: Varies
Comparison: Local vs Cloud AI
| Feature | Local (My Stack) | Cloud AI (Claude/GPT) |
|---|---|---|
| Privacy | 🔒 100% private | Data goes to cloud |
| Cost | Free after setup | $20-200/month |
| Works Offline | ✅ Yes | ❌ Needs internet |
| Model Size | 3B parameters | 100B+ parameters |
| Smartness | Good for basics | Genius level |
| Context Window | 4K-8K tokens | 100K+ tokens |
| Multimodal | ❌ Text only | ✅ Images, files |
| Setup Time | 30 minutes | 5 minutes |
When to Use Local AI
- Quick code edits and refactoring
- Learning and experimentation
- Privacy-sensitive projects
- Offline work
- Budget-conscious developers
When to Use Cloud AI
- Complex problem-solving
- Large codebase understanding
- Multimodal tasks (images, files)
- Latest information retrieval
- Production-grade code
The Future of Local AI
The local AI space is evolving rapidly. Here's what's coming:
- Better models - 7B and 8B models will run smoothly on 16GB+
- Specialized models - Code-specific, embedding, and vision models
- Fine-tuning - Train models on your private codebase
- More tools - MCP integrations for enhanced capabilities
If you're on a budget or care about privacy, now is a great time to start.
Getting Started
Ready to try it yourself? Here's how:
Step 1: Install Ollama
curl -fsSL https://ollama.com/install | shStep 2: Pull a Model
# My recommendation - good balance of speed and capability
ollama pull llama3.2
# Lighter option
ollama pull llama3.2:1b
# Alternative model
ollama pull phi3.5Step 3: Install OpenCode
Visit opencode.ai for your platform's installation instructions.
Step 4: Connect Them
Configure OpenCode to use Ollama as the model provider. (See OpenCode documentation for details.)
Conclusion
I've been using this local setup for a few weeks now, and I'm genuinely impressed. It handles most of my daily development tasks - writing helper scripts, debugging code, explaining concepts - without ever needing to touch the cloud.
Is it as smart as Claude? No, not even close.
But it doesn't need to be. For quick tasks, offline work, and privacy-conscious development, it's perfect.
And the best part? It runs on an 8GB laptop - the same machine I use for everyday work.
What do you think? Would you try running AI locally? What's your stack? Let me know!
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