training-6-inference-1-2-flops-per-parameter-per-token

Summary

Building a large language model is roughly three to six times more computationally expensive per token than running it, which means the upfront training bill dwarfs the per-request serving cost. This ratio anchors most cost-planning decisions: whether to fine-tune from scratch or adapt an existing model, and how many tokens a deployment will actually serve over its lifetime.