I Want Everything Local – Building My Offline AI Workspace

Last updated: 2025-08-09

The wake-up call

My journey toward a local AI setup started with a frustrating evening last winter. I was deep in a coding session, relying heavily on Claude and ChatGPT for help with a complex refactoring task, when my internet went out. Suddenly, I couldn't code effectively. That dependency bothered me more than it should have. When I read about someone else building their offline AI workspace on Hacker News, I knew I had to try it myself.

Why I wanted everything local

The motivation was both practical and philosophical. On the practical side, I was tired of:

• API rate limits interrupting my flow state
• Monthly subscription costs adding up ($60+ for various AI tools)
• Sending my code to external services, especially for client projects
• Spotty internet making my tools unreliable

But there was also something deeper. I wanted to understand these AI models, not just consume them as black box services. Running models locally forces you to learn about their actual capabilities and limitations in ways that using ChatGPT through a web interface never will.

My hardware setup

I started with what I had: a 2021 MacBook Pro with 32GB RAM. Not ideal for larger models, but sufficient for experimentation. For more demanding work, I set up a secondary machine:

• Primary development: MacBook Pro (M1 Max, 32GB) for most coding tasks
• Model hosting: Custom desktop (RTX 4070, 64GB RAM) for running larger models
• Storage: 2TB external SSD for model storage and datasets

The total hardware investment was around $2,500, but most of that would have been spent on development hardware anyway.

Software stack that actually works

After months of experimentation, here's what I settled on:

Model hosting and management

• Ollama: The easiest way to run models locally. Installation is simple, model management is straightforward.
• Text Generation WebUI: For more advanced model configuration and fine-tuning experiments.
• LM Studio: User-friendly interface for trying different models quickly.

Development integration

• Continue.dev: VS Code extension that connects to local models. Game-changer for coding assistance.
• Local API proxy: Custom FastAPI service that normalizes different local models to match OpenAI's API format.
• Custom scripts: Python utilities for model switching, performance monitoring, and resource management.

Models I actually use

• Code Llama 13B: My daily driver for code completion and simple refactoring
• Mistral 7B Instruct: Great for general questions and documentation
• Wizard Coder 15B: When I need more sophisticated code analysis
• Llama 2 70B (quantized): For complex reasoning tasks, though it's slow on my hardware

What surprised me most

The biggest surprise was how much my relationship with AI changed. When every query has a small computational cost (in terms of time and electricity), you become much more intentional about what you ask. I stopped using AI as a crutch for things I could figure out myself and started using it for genuinely difficult problems.

I also discovered that smaller, specialized models often perform better than large general-purpose ones for specific tasks. Code Llama 13B gives me better code completions than GPT-4 in many cases, and it runs instantly on my local hardware.

The practical challenges

Local AI isn't all sunshine and rainbows:

• Model management is tedious: Downloading, organizing, and switching between models takes time and storage space.
• Performance varies wildly: Some models are amazing, others are frustratingly bad. Finding good models requires lots of testing.
• Resource intensive: Running larger models makes my laptop's fans spin up and drains the battery quickly.
• Staying current is hard: New models come out frequently, but evaluating them properly takes significant time.

Where local models excel

After using this setup for eight months, here's where local models genuinely shine:

• Code completion: Faster than cloud services and surprisingly good quality
• Simple refactoring: Renaming variables, restructuring functions, basic cleanup
• Documentation: Generating docstrings and comments for existing code
• Brainstorming: Exploring different approaches to problems without API costs
• Learning: Understanding how different model architectures affect output quality

Where they still fall short

Local models struggle with:

• Complex reasoning: Multi-step problems that require holding lots of context
• Recent knowledge: Anything that happened after their training cutoff
• Domain expertise: Specialized knowledge in niche areas
• Large context windows: Most local models have much smaller context limits than GPT-4

The hybrid approach I've landed on

Pure local setup was interesting as an experiment, but I've found a hybrid approach more practical:

• 80% local: Code completion, basic refactoring, simple questions
• 20% cloud: Complex architecture discussions, debugging tricky issues, areas requiring very recent knowledge

This reduces my cloud AI costs by about 75% while maintaining productivity for difficult problems.

Would I recommend it?

For most developers, probably not as a complete replacement for cloud services. But as a complement? Absolutely. Local models are excellent for routine coding tasks, provide valuable insights into how AI actually works, and offer genuine privacy benefits for sensitive projects.

If you're curious about AI internals, want to reduce dependency on external services, or just enjoy tinkering with new technology, building a local AI workspace is a rewarding project. Just don't expect it to completely replace cloud services – at least not yet.

The technology is advancing rapidly though. Models that required high-end hardware a year ago now run on laptops. I suspect local AI will become much more practical for mainstream use within the next few years.