Understanding the critical differences between AI training and inference, and why each requires specialized hardware optimization
Learning Phase
Teaching the model to recognize patterns and relationships in data
Happens once (or periodically for updates)
Massive datasets (terabytes to petabytes)
Extremely high computational requirements
Days, weeks, or months to complete
Application Phase
Making predictions and decisions on new, real-world data
Millions to billions of times daily
Small individual data points (KB to MB)
Moderate but optimized for speed and efficiency
Milliseconds to seconds per request
Side-by-side analysis of training versus inference requirements
| Aspect | Training | Inference |
|---|---|---|
| Primary Goal | Learn patterns from historical data | Apply learned patterns to new data |
| Hardware Optimization | High memory bandwidth, parallel processing | Low latency, energy efficiency, throughput |
| Batch Size | Large batches (32-512+ samples) | Small batches (1-32 samples) |
| Cost Structure | High upfront cost, one-time expense | Ongoing operational costs, scales with usage |
| Energy Consumption | Very high during training period | Moderate but continuous |
| Precision Requirements | High precision (FP32/FP16) | Can use lower precision (INT8/INT4) |
| Memory Usage | Stores gradients, activations, optimizer states | Only stores model weights and activations |
| Scalability Focus | Scale up (bigger models, more data) | Scale out (more concurrent requests) |
Why inference dominates AI operational expenses
Training: Happens once, costs millions upfront
Inference: Happens billions of times, costs compound rapidly
Running a large language model consumes more energy over its lifetime than an average American car, with 90% coming from inference, not training.
Why one size doesn't fit all in AI hardware
Since 90% of your AI costs come from inference, shouldn't your hardware be optimized for it?