AI inference costs at scale depend critically on GPU memory bandwidth and compute efficiency; organizations can use napkin math calculations to predict per-user costs and determine GPU cluster capacity by understanding matrix multiplication operations and attention mechanisms in language models. With proper optimization techniques like KV-caching and batching, companies can serve hundreds of concurrent users on a single GPU, but memory bandwidth—not raw compute power—becomes the limiting factor for cost-effective inference at scale. This has significant implications for IT organizations sizing infrastructure investments, as the cost-per-inference token directly impacts product profitability and infrastructure ROI when deploying AI services.
AI inference costs at scale depend critically on GPU memory bandwidth and compute efficiency; organizations can use napkin math calculations to predict per-user costs and determine GPU cluster capacity by understanding matrix multiplication operations and attention mechanisms in language models. With proper optimization techniques like KV-caching and batching, companies can serve hundreds of concurrent users on a single GPU, but memory bandwidth—not raw compute power—becomes the limiting factor for cost-effective inference at scale. This has significant implications for IT organizations sizing infrastructure investments, as the cost-per-inference token directly impacts product profitability and infrastructure ROI when deploying AI services.
