Guide: Run LLMs on hourly rented GPUs with Ollama and vLLM

This guide provides a step-by-step checklist for running large language models (LLMs) on hourly rented GPUs, a cost-effective alternative to owning dedicated hardware. It details how to select a GPU based on model size and VRAM requirements, verify the setup with `nvidia-smi`, and deploy models using tools like Ollama for `llama3.1:8b` or vLLM for OpenAI-compatible APIs with models such as `Qwen/Qwen2.5-7B-Instruct`. The guide emphasizes shutting down rented resources promptly to avoid ongoing costs, noting that storage volumes incur separate charges even when idle. AI

IMPACT Enables individuals and smaller teams to experiment with large language models without significant hardware investment.

RANK_REASON The item is a how-to guide for using cloud GPU rental services to run LLMs, not a release of a new model or significant industry development.

Read on dev.to — LLM tag →

• graphics processing unit
• Hostinger
• llama3.1:8b
• Ollama
• Qwen/Qwen2.5-7B-Instruct
• RunPod
• virtual private server
• vLLM

• infra

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