I Stopped Paying for Colab Pro and Run LLMs on My Own GPU

Описание и применение

Автор отказался от подписки Colab Pro и запустил все задачи LLM на собственном GPU RTX 3060, снизив ежемесячные расходы с $49.99 до $0, сохранив при этом производительность и локальность данных.

Задача

High monthly cost of Colab Pro for running LLM inference and fine‑tuning on a consumer GPU.

Результат

Monthly cost dropped from $49.99 to $0, with no disconnections or idle timeouts, and all data remains local.

Рабочий процесс

• 1. Install Ollama and pull desired model (e.g., qwen2.5:7b-instruct-q4_K_M).
• 2. Use Unsloth with the 3060 to fine‑tune models (e.g., 7B QLoRA).
• 3. Run inference locally via Ollama API or JupyterLab notebooks.
• 4. For occasional GPU memory overflow, use free Colab T4 tier.
• 5. Monitor power usage (~170 W) and adjust usage to keep electricity cost low.

Доказательства

Total: $0/month vs $49.99/month. The only thing I still use Colab for is the occasional T4 session when I need CUDA memory I don't have — and the free tier covers that.

Источник

Compute units burned: ~1,800/month (mostly idle timeouts I forgot to kill). Actual productive GPU hours: ~35/month. Effective cost per productive hour: $1.43.

Источник