Artificial intelligence training and artificial intelligence inference are two key elements of the machine learning development lifecycle. The training phase, sometimes called the development phase, involves feature engineering, selection and model training. Inference occurs after the training is complete. During inference, the model is introduced to unseen, real-world scenarios and uses its learning to make accurate predictions.

While both concepts seem similar and have overlapping workings, they have distinct objectives, resource requirements and performance considerations. Understanding the difference allows ML engineers to develop requirement-specific architectures and optimize the overall workflow.

This article will discuss both concepts in detail. We will first understand their purpose and specific requirements and then compare AI inference vs training further.

What is AI trainingWhat is AI training

Understanding what is AI training requires knowledge of the fundamentals of machine learning. It refers to the process of teaching an ML model to recognize patterns within a dataset. The model further learns to associate these patterns with an output (target) variable, which it predicts during inference. The AI training phase consists of the following steps:

• Data collection: Data is collected from various sources, such as APIs, live digital systems or online survey forms. This data contains real-world information that helps the algorithm understand and model real-world patterns.

• Pre-processing: Data collected in the previous stage contains numerous anomalies, such as inaccuracies, outliers and biases. The pre-processing stage corrects these errors, ensuring only clean data reaches the model.

• Model selection: The next step is to select the right algorithm for optimal model training. Simpler datasets are better modeled using basic ML models, while complex, extensive datasets require deep neural network architecture.

• Iterative training: The last step is to train the model using the selected algorithm and cleaned dataset. The training phase iterates over each data sample, calculates the error and uses it to adjust its internal weights until the maximum number of iterations is reached.

Most modern AI models are trained datasets consisting of millions of samples and hundreds or even thousands of features, depending on the problem. Such training procedures require expensive GPUs and can take several hours to a few days to complete.

Tabular data problems are generally simpler, usually consisting of less than a hundred features per dataset. However, complex domains like text processing and computer vision require specialized neural network architectures that capture features from unstructured information. Some popular architectures include Convolutional Neural Networks (CNNs) and Transformers, designed to analyze contextual information while making predictions.

What is AI inferenceWhat is AI inference

Once the model is trained, it is deployed as a standalone application or integrated into a larger system. During deployment, the model is fed real-world information, often in real-time. This data is entirely new for the model, and it must use the knowledge gained during training to make predictions. This act of analyzing unseen information and drawing conclusions is known as AI inference.

The inference environment is usually a cloud server or an edge device where the model resides. This environment is equipped with the necessary hardware (CPUs or GPUs) and connected to the relevant data touchpoints via extract, transform and load (ETL) pipelines. The pipelines may deliver the data in real time: as the information is generated, it is processed and delivered to the model. Moreover, the architecture may also utilize batch processing, i.e., the system collects data over a set period and then delivers it to the model simultaneously for predictions.

Some common examples of AI inference systems include facial recognition devices and voice assistants. Both are edge-deployed solutions, as the models reside on specialized devices and capture information via sensors like cameras or microphones. For example, in the case of a facial recognition system, the edge device captures a person’s image and sends it over to the model. The model then runs inference over this image to determine whether the person is registered.

AI inference vs training — what’s the differenceAI inference vs training — what’s the difference

Let’s understand the key differences between the two processes.

Computational resource needsComputational resource needs

AI training is an iterative and experimental phase involving various feature sets and AI algorithms. Such experimentation requires heavy computational power since it may involve complex neural network-based algorithms like transformers and vision transformers. Moreover, during training, the model is exposed to an extensive dataset multiple times, and it requires a large VRAM and parallel processing capacity to perform all calculations in a reasonable timeframe.

ML engineers often pair multiple GPUs (usually 10 or 20, but modern LLM trainings have also utilized several thousands) for adequate training performance to gain a larger VRAM buffer and maximum computing power.

AI inference, on the other hand, processes significantly fewer calculations and does not require as much computing power. Depending on the application scale, a deployed model experiences a few hundred or thousand requests simultaneously. This is significantly less than the millions of data points it processes during training. Secondly, the inference process only involves a forward pass (no backpropagation/error calculation), reducing the calculation complexity.

Due to this, AI inference requires fewer computational resources and can usually work with a single GPU or even a CPU. Many edge devices, like facial recognition devices, perform AI inference on mobile-grade CPUs, signifying their low resource requirement.

TimeframeTimeframe

Training an AI model can take several days or weeks, depending on the model’s complexity, dataset size and available computation resources. For example, a (now deleted) social media post revealed that GPT-4 training took between 90 and 100 days.

However, inference times are usually a few seconds or even milliseconds, depending on how critical the task is. Many modern AI services depend on real-time inference. For example, an autonomous vehicle analyzes multiple objects simultaneously to navigate itself, and the slightest delay in inference can be critical.

Energy and cost implicationsEnergy and cost implications

Since AI training requires 10s to 1,000s of GPUs and months’ worth of training, the process incurs a high energy and monetary cost. It is estimated that GPT-3 training consumed 1,287 megawatt-hours (MWh) of electricity. To put this into perspective, this is about as much power as consumed annually by 130 US homes.

Moreover, the monetary cost of most modern LLM development is estimated to be around 10s of millions of dollars. The development cost for GPT-4 is estimated at around 70 million USD, while Gemini 1 was over 150 million USD.

Processes in training and inferenceProcesses in training and inference

Processes that are more effective in trainingProcesses that are more effective in training

• Learning complex data patterns: The training stage is the heart of an AI solution as it helps the model understand real-world data patterns and solve complex problems. It examines millions of data points, covering several scenarios and helps the model learn from its own mistakes (errors).

• Scalable training environment: AI trainings are often conducted in a scalable environment. These environments automatically scale up their resources as the dataset grows to ensure efficient training.

Processes that are less effective in trainingProcesses that are less effective in training

• Data collection and cleaning efforts: Training a robust model requires a high-quality, extensive dataset. However, creating such a dataset requires effort, time and patience, and the overall process can be exhausting. Moreover, failure to create quality data can hinder model performance, causing the overall project to fail.

• High training costs: AI trainings have high money, time and resource costs. They require expensive GPUs and long training times, making the process unaffordable for many.

• Optimizing the training: The training process requires various optimizations like feature selection, hyper-parameter optimization and architecture modification. The optimization process is mostly trial and error and can be tiring.

Processes that are more effective in inferenceProcesses that are more effective in inference

• Real-time decision making: This allows applications to make real-time decisions based on the data inputs. These decisions drive key use cases like autonomous vehicles and facial recognition systems.

• Scalable inference environment: Inference environments are often scalable as well. The deployment environment increases resources as the user base grows.

Processes that are less effective in inferenceProcesses that are less effective in inference

• High latency: Unoptimized model architectures or weak hardware increase the inference time. Moreover, cloud-deployed solutions can often experience network latency in the inference process. These issues can ruin user experience or have critical impacts in domains like healthcare or autonomous vehicles.

• Performance depends on training: If the model did not undergo adequate training, its poor performance is reflected during inference. This cannot be fixed during inference, and developers must revisit the training phase.

• Constant re-training: Inference performance can deteriorate due to model drift. Due to this, AI models need to be constantly retrained to maintain inference accuracy.

Choosing the right hardware for training and inferenceChoosing the right hardware for training and inference

We have already discussed the distinct hardware requirements for training and inference. While the former requires various high-performing GPUs, the latter is often executable on simpler hardware. However, the exact hardware requirements depend entirely on the business use case.

Some of the simpler AI use cases can be built entirely on CPUs, while some more complex applications, like LLM-based chatbots, can require several GPUs for inference. The table below summarizes a few scenarios and their potential hardware requirements.