GPUs: Rent vs. Buy (cloud-gpus.com)

The article "GPUs: Rent vs Buy" explores the decision-making process for those interested in self-managed LLM inference, focusing on the trade-offs between purchasing and renting high-end GPUs. As GPU prices increase and smaller LLMs become more capable, the analysis highlights the importance of hardware access and data privacy. For users prioritizing steady, private access to powerful GPUs, purchasing may be more cost-effective in the long run, particularly with ongoing price inflation and intermittent availability in cloud services. Notably, consumer-grade GPUs, like the NVIDIA RTX 5090, provide competitive performance for under $20,000 compared to renting higher-end chips, which can exceed $1.5 per hour. On the other hand, for those anticipating a potential drop in prices or limited usage, renting remains a viable option, especially for intermittent tasks. The article categorizes LLM inference workloads—from basic chat interactions to complex coding tasks—emphasizing that the choice between buying and renting depends heavily on individual usage patterns and hardware requirements. It also suggests consumers evaluate performance through short-term rentals before committing to a purchase, accommodating different technical specifications and configurations for optimal inference success.