AI Inference vs. Training: What They Are and How They Differ

AI inference and training are terms you'd run into if you have been around software engineering or even just scrolled through the news. Both are integral to delivering the AI-powered experiences we have come to expect from many of the applications we use daily.

According to McKinsey, by 2030 inference will overtake training as the dominant workload in AI data centers, making up more than half of all AI compute and roughly 30-40% of total data center demand.

In this post, we will break down what AI training and inference are, the steps involved in each, and how they differ.

What is AI inference?

Setting AI aside for a second, inference is the logical process of drawing conclusions from existing evidence, observations, and background knowledge. In the context of AI, inference is the process by which a pre-trained model takes new, unseen data and produces a prediction or decision based on what it learned during training.

Pre-trained here means the model has already completed training; its internal weights are adjusted to recognize patterns in the data, and those weights are fixed. The model is no longer learning. It is applying what it already knows.

A useful way to think about it is that training is the model of studying for an exam. Inference is the model sitting the exam and answering questions it has not seen before, using what it learned during training.

Steps involved in AI inference

Once a model's training is complete, the inference process generally follows these steps:

Model deployment: The trained model is packaged and deployed to a serving environment. Deployment can be a cloud server, an on-premises GPU cluster, or an edge device such as a phone or camera.
Input ingestion: New data arrives at the model. Input could be a user typing a prompt into a chatbot, a camera capturing an image, or a sensor reading from an IoT device.
Pre-processing: The input is converted into a format the model expects. For text models, this usually means tokenization, which is the process of breaking the input into smaller units. For image models, this could involve resizing or normalizing pixel values.
Forward pass: The pre-processed input moves through the model's layers. The model applies its learned weights to the data and produces an output. Unlike training, there is no backward pass and no weight updates, only a single direction of computation.
Post-processing and response: The raw output is converted back into something the application can use. For a chatbot, this means turning token IDs back into readable text. For an image classifier, it could mean mapping a probability score to a category label.

Use cases for AI inference

If you have searched for anything on Google recently and seen the AI Overview at the top of the results, or asked a chatbot to summarize an article, you have already seen AI inference at work.

Both are examples of pre-trained models that take your input and produce a response in real time. The large language models powering them were trained on huge datasets months before you ever opened the browser. Every query you send runs through a forward pass on that frozen model, which is the inference step. Your input does not change the model. It just gets processed by it.

A few more places inference shows up in:

Recommendation systems: Streaming platforms like Spotify and Netflix use inference to predict what you might want to watch or listen to next based on your viewing or listening history.
Fraud detection: Banks run transactions through trained models in milliseconds to flag suspicious activity before a payment clears.
Computer vision: Phones use inference for face unlock, object detection, and real-time photo enhancements.

The AI inference market is projected to grow from $106 billion in 2025 to $255 billion by 2030, and Gartner expects more than half of AI-optimized IaaS spending to support inference workloads by 2026.

What is AI training?

In the same way inference has a meaning outside of AI, training does too. To train is to teach, prepare someone to perform a task well. The same definition holds in AI, with the student being a machine learning model and the teaching material being data.

AI training is the process of feeding a model large amounts of data so it can learn the patterns within that data and adjust its internal weights to make accurate predictions. The weights start as random values, and through repeated exposure to examples, they get nudged toward values that minimize the model's errors. By the end of training, the weights are tuned well enough that the model can be deployed and used for inference.

Going back to the exam analogy… If inference is the model sitting the exam, training is the months of revision that came before it: the textbooks, the practice questions, the mock papers. The model studies, gets things wrong, corrects itself, and gradually builds the knowledge it needs to perform.

Steps involved in AI training

Most AI training pipelines follow a similar set of steps, regardless of whether the model is a small classifier or a frontier language model:

Data collection: Training starts with assembling a dataset relevant to the problem. For a language model, this could be a large blob of text scraped from the web, books, and code repositories. For a medical imaging model, this could be thousands of labeled X-rays.
Data preparation: Raw data is rarely usable as-is. It needs to be cleaned, de-duplicated, labeled where necessary, and split into training, validation, and test sets. The validation set helps tune the model during training, and the test set measures how well it performs on data it has not seen.
Model architecture selection: The architecture defines how the model is structured. Convolutional neural networks work well for images, transformers dominate language and increasingly other domains, and simpler architectures like decision trees still hold up for tabular data. The choice depends on the problem and the data.
Forward propagation: A batch of training data moves through the model's layers, and the model produces a prediction. At the start of training, these predictions are mostly wrong, since the weights are still random.
Loss calculation: The model's prediction is compared to the correct answer using a loss function. The loss is a number that quantifies how far off the prediction was. A high loss means the model got it badly wrong. A low loss means it got close.
Backpropagation and weight updates: The loss is propagated backward through the model, and an optimizer adjusts the weights to reduce the error on the next pass. This is the step that does not exist in inference. It is also what makes training so computationally expensive.
Iteration: Steps four through six repeat across the dataset, often for many passes (called epochs), until the model's performance on the validation set stops improving.
Evaluation: Once training is complete, the model is tested on the held-out test set to confirm it generalizes to data it has never seen. If it performs well, it is ready for deployment.

Use cases for AI training

Every AI product you have used started as training data. When OpenAI released GPT-4, when Google launched Gemini, when Anthropic shipped Claude, the headline moment was the end of a months-long training process that cost tens or hundreds of millions of dollars. GPT-3's training run is estimated to have consumed 1,287 megawatt-hours of electricity, roughly what 130 US homes use in a year. GPT-4's training is estimated at around $70 million, and Gemini 1 at over $150 million.

These numbers are for foundation models, which represent some of the largest and most expensive end of the spectrum. Training also happens at smaller scales every day:

Fine-tuning: Companies take an existing foundation model and train it further on their own data, so it performs better on specific tasks like customer support, code review, or legal document analysis.
Recommendation models: Streaming and e-commerce platforms retrain their recommendation systems regularly as user behavior changes and new content gets added.
Fraud detection: Banks retrain fraud models as new attack patterns emerge, so the model keeps up with how fraudsters operate.