Inference changes the question from training cost to cost per answer

Training adjusts a model’s parameters; inference uses a trained model to produce an output. Both require computation, but their operating requirements can differ. A growing number of model requests is not, by itself, evidence of a particular chip-market share.

Define the unit of value

Useful analysis asks how much computing, energy and memory are needed for an acceptable response. Latency, model size and quality requirements affect the answer. Comparing systems requires consistent workloads and explicit measurement conditions.

Forecasts need assumptions

We do not publish a numerical inference-demand forecast here. A credible estimate would need sourced assumptions for request volume, computational intensity, hardware utilization and deployment mix. Any future scenario will be labeled as a forecast rather than a reported result.