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

说明与用途

开发者将所有 AI 工作迁移到本地 Ollama 堆栈，消除了云 API 成本，保障了隐私，并在 RTX 3060 上实现了竞争性的速度。

问题

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

结果

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

工作流

• 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

证据

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

来源

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

来源