Training vs Inference

Training vs Inference

When people talk about AI, they often lump training and inference together, but they are two very different processes with different requirements. Understanding the difference is key to understanding how AI systems work and why they need the hardware they do.

Training is the learning phase. This is when you take a raw model with random weights and feed it billions of examples. The model makes predictions, compares them to the correct answers, and adjusts its weights to be more accurate. Training is incredibly compute intensive. Training a model like GPT-4 requires thousands of GPUs running for weeks or months, consuming megawatts of power and costing tens of millions of dollars.

During training, the model needs to process data in both directions. It runs the data forward to make predictions, then runs the error backward to update the weights. This back and forth, called backpropagation, requires storing all the intermediate values, which means massive amounts of memory. Training also requires high precision math, typically 16-bit or 32-bit floating point, to maintain accuracy across millions of updates.

Inference is the usage phase. This is when you take a trained model and actually use it. You give it a prompt, it runs the data forward through the network once, and produces an answer. No backpropagation, no weight updates, just a single pass through the model. Inference is much less demanding than training, but it still requires significant compute, especially for large models.

The hardware requirements differ too. Training needs high end GPUs with lots of VRAM and high bandwidth interconnects to work together. Inference can run on more modest hardware. A model that took thousands of GPUs to train might run on a single consumer GPU, or even on a phone after quantization. This is why companies train once and then distribute the trained model for millions of users to run inference.

There is also a middle ground called fine-tuning, where you take a pre-trained model and do a small amount of additional training on a specific dataset. This requires less compute than full training but more than inference. Fine-tuning is how you take a general model and specialize it for a particular task, like medical diagnosis or legal document analysis.