An AI engineer details the challenges of accurately calculating hardware requirements for on-premise LLM deployments. Initial estimates using a popular calculator for a GPT-OSS-120B model on two RTX Pro 6000 Blackwell GPUs predicted 5000 tokens/sec, but real-world performance was five times slower. The article explains how to properly assess LLM resource needs, especially with non-standard hardware, and describes a rigorous testing process to provide clients with reliable performance guarantees. AI
IMPACT Highlights the difficulty in accurately provisioning hardware for on-premise AI, potentially impacting enterprise adoption costs and timelines.
RANK_REASON Article details a specific technical challenge and methodology for on-premise LLM deployment, akin to a technical paper or case study. [lever_c_demoted from research: ic=1 ai=1.0]
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