Setting Up Ollama Locally: A Developer's Privacy-First AI Stack

Description and use

A developer migrated all AI workflows to a local Ollama stack, eliminating cloud API costs, preserving privacy, and achieving competitive speed on an RTX 3060.

Problem

High cost, privacy concerns, and latency of cloud AI APIs for developers.

Outcome

Local inference cost‑free, privacy‑preserving, and competitive in speed for 7B models on an RTX 3060.

Workflow

• Install Ollama via curl script
• Pull desired models (codellama:13b, llama3:8b, mistral:7b)
• Use local API at localhost:11434 with existing OpenAI-compatible code or the official Ollama Python library
• Select appropriate model per task (code review, quick questions, writing/analysis)
• Monitor performance (tokens/sec) and adjust GPU/VRAM usage

Evidence

I was spending $40-60/month on API calls. Now it's $0.

Source

For reference, GPT-4 API typically returns 30-50 tokens/sec. So local 7B models are competitive on speed.

Source