Cost Optimization for GPU-Based LLM Development
Published:Jan 3, 2026 05:19
•1 min read
•r/LocalLLaMA
Analysis
The article discusses the challenges of cost management when using GPU providers for building LLMs like Gemini, ChatGPT, or Claude. The user is currently using Hyperstack but is concerned about data storage costs. They are exploring alternatives like Cloudflare, Wasabi, and AWS S3 to reduce expenses. The core issue is balancing convenience with cost-effectiveness in a cloud-based GPU environment, particularly for users without local GPU access.
Key Takeaways
- •The primary concern is minimizing costs associated with data storage when using GPU providers.
- •The user is exploring alternatives to Hyperstack for cheaper storage solutions.
- •The user is seeking advice on cost-effective strategies for building LLMs without local GPU access.
Reference
“I am using hyperstack right now and it's much more convenient than Runpod or other GPU providers but the downside is that the data storage costs so much. I am thinking of using Cloudfare/Wasabi/AWS S3 instead. Does anyone have tips on minimizing the cost for building my own Gemini with GPU providers?”