“Training” and “inference” get used loosely, but they’re genuinely distinct phases with very different computational profiles and cost structures — understanding the difference clarifies a lot of AI infrastructure decisions.
Training: Learning the Weights
Training feeds the model enormous amounts of data, repeatedly adjusting its internal parameters (weights) to reduce prediction error — genuinely computationally massive, often requiring thousands of GPUs running for weeks, and representing the overwhelming majority of the total cost behind building a large model in the first place.
Inference: Using the Trained Model
Training: data + current weights -> compute gradients -> UPDATE weights
(weights change; this happens ONCE, or periodically for fine-tuning)
Inference: input + FIXED, already-trained weights -> output
(weights DON'T change; this happens EVERY time someone uses the model)
Why This Split Matters for Cost and Hardware
Training happens rarely (once per model version) but is extraordinarily compute-intensive per run. Inference happens constantly (every single user request) but is comparatively cheap per individual request — though at truly massive scale (millions of daily requests), cumulative inference cost can genuinely exceed the original training cost over the model’s operational lifetime. This split is exactly why some hardware is specifically optimized for training (high memory bandwidth, large clusters with fast interconnects) while other hardware targets inference specifically (lower latency, better cost-per-request at scale, sometimes lower precision).