analiza aiqbench

The article focuses on benchmarking the cost-efficiency of running a specific large language model (GLM-5.2) across different hardware configurations, which is a core concern for practical machine learning deployment and inference optimization. By analyzing token costs per GPU, it provides actionable data for developers evaluating the economic viability of deploying frontier models without relying on cloud APIs.