Local AI Economics
When building your own hardware makes sense
The question
Cloud AI subscriptions add up. If you're spending $60/month on ChatGPT Plus + Claude Pro + Cursor, that's $720/year. Heavy API usage can push that to $300-500/month. At some point, owning hardware beats renting.
What a local cluster costs
A 4x Framework Desktop setup with 128GB RAM each (512GB total, ~384GB usable for models):
Break-even math
Light user ($100/month cloud): $12,000 / $1,200 annual savings = 10 years. Not worth it.
Heavy user ($400/month cloud): $12,000 / $4,800 annual savings = 2.5 years. Viable.
Team ($700+/month cloud): $15,000 / $8,400 annual savings = 1.8 years. Strong case.
What you can run locally
With 384GB addressable memory across the cluster:
- Llama 3.3 70B - fits in ~40GB
- Qwen 2.5 72B - similar requirements
- DeepSeek V3 - with quantization
- Llama 405B - distributed across nodes
- Any custom fine-tuned models you build
Local gets you ~10ms latency vs ~200ms cloud. No rate limiting. No quotas. No sending your code to someone else's servers.
When to build local
- Monthly cloud costs exceed $300-400
- You care about data privacy
- You want to experiment without watching the meter
- You're comfortable managing hardware
- You're planning 3+ years of heavy AI use
When to stay cloud
- Monthly costs under $200
- You need the absolute latest models immediately
- You don't want to maintain hardware
- Your usage is casual or project-based
The non-financial stuff
Independence has value that's hard to put a number on. You're not subject to price increases, service changes, or rate limiting during critical work. Your infrastructure stays yours.
The flip side: when hardware fails at 2am on a deadline, there's no support ticket to file.