What Is AI Inference?
AI inference is the process of using a trained AI model to produce a result from new input.
During training, an AI model learns patterns from data. After training, the model can process new input and produce a result. This is called inference.
In simple terms, training builds the model, while inference uses the trained model.
How Does AI Inference Work?
AI inference follows a simple process:
Input → Preprocessing → Model → Computation → Output
Each step helps the AI turn new data into a useful result.
1. Input
First, the AI receives new data.
The input can be different types of data, such as:
- Text
- Images
- Audio
- Video
For example, when you ask an AI chatbot a question, your question is the input. An image recognition system may receive a photo as its input.
2. Preprocessing
The raw input usually needs to be prepared before the AI model can process it.
This step is called preprocessing.
Depending on the type of data, preprocessing may include resizing an image, preparing text, or converting data into a format the model can process.
The goal is to put the input into a suitable format for the AI model.
3. Model
Next, the prepared input is sent to a trained AI model.
The model uses the information it learned during training to process the new input. Its learned weights and other parameters are used during this process.
During normal inference, the model's learned weights are not changed. The model uses what it has already learned to produce a result.
4. Computation
The model then performs many calculations on the input.
These calculations are needed to determine the most likely result.
For example, an image model may calculate which object is most likely to appear in an image. A language model may calculate which words or tokens should come next when generating a response.
5. Output
Finally, the model produces an output.
The output depends on the type of AI system and the task.
It can be:
- Text
- A classification result
- A prediction
- A recommendation
- An image
- Speech
In simple terms, AI inference takes new input, processes it through a trained model, and produces an output.
Where Is AI Inference Used?
AI inference is used in many everyday AI systems. The basic process is simple: new data goes into a trained AI model, and the model produces a result.
Here are some common examples.
Chatbots
User question → AI inference → Generated answer
When you ask a chatbot a question, the trained AI model processes your prompt and generates a response.
Inference allows chatbots to answer questions, write text, help with coding, and perform other tasks without retraining the model for every request.
Image Recognition
Image → AI inference → Object or classification result
Image recognition systems use inference to analyze new images or video.
For example, an AI system can identify objects in a photo, detect defects in a product, or recognize patterns in a medical image.
Voice Assistants
Speech → AI inference → Recognized words or response
Voice assistants use AI inference to process spoken commands.
The system can recognize what a person says, understand the request, and produce a response or perform an action, such as playing music or controlling a smart device.
Recommendation Systems
User activity → AI inference → Recommended content or product
Recommendation systems use inference to predict what a user may be interested in.
They can process information such as searches, clicks, or past purchases and use it to recommend videos, products, articles, or other content.
Fraud Detection
Transaction data → AI inference → Risk prediction
Financial systems can use AI inference to check transactions for unusual patterns.
The model can analyze information about a transaction and estimate whether it may be risky. This can help financial companies identify suspicious activity and take action.
Autonomous Systems
Sensor or camera data → AI inference → Decision or action
Autonomous systems use inference to understand information from cameras and other sensors.
For example, an autonomous vehicle can analyze its surroundings and provide information that helps the vehicle slow down, stop, or change direction.
In all of these examples, the basic idea is the same:
New data → AI inference → Useful result
This is why AI inference is an important part of many AI services and systems.
What Affects Inference Speed?
AI inference speed can vary depending on several factors. The model size, hardware, input size, number of users, and optimization methods can all affect how quickly an AI system produces a result.
Model Size
The size of an AI model can affect inference speed.
A smaller model usually has fewer parameters and requires fewer calculations. This can make it faster and more efficient.
Larger models have more parameters, so they usually require more calculations and memory to process each input.
However, model size is not the only factor. The hardware, input size, and software used to run the model can also affect inference speed.
Hardware
The hardware used to run an AI model can make a big difference.
CPUs can handle many different tasks, but GPUs are often better suited to the large number of parallel calculations used by AI models.
GPUs can perform many calculations at the same time. This makes them useful for AI inference, especially when working with large models.
AI accelerators are specialized hardware designed to speed up AI workloads. They can be used in data centers, computers, smartphones, and other devices.
Input Size
The size of the input can affect inference speed.
Longer text and larger images usually require more processing.
Number of Users
The number of users can also affect inference speed.
If only one person is using an AI service, the system may have plenty of computing resources available for that request.
However, thousands of people may send requests at the same time. The system then has to share its computing resources between many requests.
This can increase waiting time.
AI services often use techniques such as batching, which allows multiple requests to be processed together. This can help the system handle more requests efficiently.
What Is Throughput?
Throughput means how much work a system can complete in a given amount of time.
For AI inference, throughput can be measured by how many tokens a system can generate per second or how many requests it can process over a period of time.
Throughput is different from latency.
- Latency: How long it takes to produce a result.
- Throughput: How much work the system can handle over time.
Both are important when many people use an AI service at the same time.
Inference Optimization
AI inference can also be made faster through inference optimization.
Inference optimization means improving the way a trained AI model runs so it can produce results faster while using computing resources more efficiently.
Common approaches include:
- Quantization: Using smaller numerical representations to reduce computing and memory needs.
- Model compression: Reducing the size of a model while trying to keep its useful performance.
- Faster hardware: Using GPUs or specialized AI accelerators.
- Efficient model designs: Using models designed to perform tasks with fewer computing resources.
These methods can help AI systems reduce response time and handle more requests without requiring as many computing resources.
In simple terms, faster inference depends on more than just the AI model. The model size, hardware, input size, number of users, and optimization methods all play a role.
Why Does AI Inference Cost Money?
AI inference costs money because running an AI model requires computing resources.
AI Servers Need Computing Resources
Large AI models can require a lot of computing power during inference.
For example, when a language model generates a response, it processes the input and generates the answer step by step. The system also needs memory to store information used during the process.
This means that every request uses computing resources.
Inference Happens Repeatedly
One important difference between training and inference is how often they happen.
Inference happens whenever people use the trained model.
For example:
1 user → 1 request → 1 inference
If thousands or millions of people use the service, the system may need to perform inference many times.
This is why inference can become a major ongoing cost for an AI service.
What Is Inference Cost Per Request?
Inference cost per request means the cost of processing one AI request.
Longer inputs and outputs can require more computing resources than shorter ones.
The cost can also depend on the model, hardware, and how efficiently the system runs.
What Is Inference Cost at Scale?
A single AI request may use a small amount of resources. But when a service receives a large number of requests, the total cost can become significant.
Inference cost at scale means the total cost of running inference as usage grows.
For example:
A small number of requests → less total computing
1 million requests → much more total computing
AI companies can reduce these costs through efficient models, caching, faster hardware, and other optimization methods.
Cloud vs. On-Device Inference
AI inference can happen in two common ways: on a remote server or directly on a device.
The best approach depends on the task, the available hardware, and other requirements.
Cloud Inference
With cloud inference, the AI model runs on a remote server.
A user sends a request over the internet. The remote server processes the request using its computing resources and then sends the result back to the user's device.
The basic process is:
Device → Internet → AI Server → Inference → Result
Cloud inference can use powerful hardware, such as high-performance GPUs and large amounts of memory. This makes it possible to run large AI models and handle many requests.
However, cloud inference usually depends on an internet connection. Network problems or a busy server can make the response slower.
On-Device Inference
With on-device inference, the AI model runs directly on a smartphone, computer, or another device.
The basic process is:
Device → Local AI Model → Inference → Result
Because the data can be processed directly on the device, some AI features can work without a constant internet connection.
On-device inference can also reduce the need to send personal data to a remote server. For example, some voice, image, translation, and text features can be processed locally.
However, devices have limited computing power, memory, battery life, and cooling compared with large data center servers. This can make it more difficult to run very large AI models on a device.
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| Figure 1. Cloud-based and on-device processing paths |
What Is the Difference?
The main difference is where the AI model runs.
Cloud inference uses a remote server, so it can take advantage of powerful hardware and large AI models. It can also handle many requests by using multiple servers. However, it usually depends on a network connection and requires the request to travel between the device and the server.
On-device inference runs the model directly on the user's device. This can reduce network delays and allow some AI features to work offline. It can also help keep data on the device. However, the AI model must work within the device's limits, such as its memory, computing power, battery, and heat.
In simple terms, cloud inference uses remote computing resources, while on-device inference uses the computing resources available on the device.
The right approach depends on the needs of the AI system. Some applications may use cloud inference, while others may use on-device inference or a combination of both.
Inference vs. Reasoning
AI inference and AI reasoning are not the same thing.
Inference is the broader process of using a trained model to produce an output from an input. Reasoning refers to solving a problem through one or more steps to reach an answer.
For example, identifying a cat in a photo is an inference task. Solving a complex problem step by step can involve AI reasoning.
Common Misunderstandings About AI Inference
AI inference can be confusing at first. Here are some common misunderstandings about it.
“Inference means the AI is learning.”
Not exactly.
Training is the process of teaching an AI model by using data and adjusting its parameters.
Inference happens after training, when the trained model processes new input and produces a result.
“Inference only applies to ChatGPT or generative AI.”
This is not true.
Inference is used in many types of AI systems.
For example, an image recognition system can use inference to identify an object in a picture. A recommendation system can use it to suggest products or content. A fraud detection system can use it to identify unusual activity.
Generative AI is only one example of where inference is used.
“A Larger AI Model Is Always Faster.”
Larger models often require more computing power and memory, which can make inference slower.
However, inference speed also depends on the hardware, input size, and optimization methods used to run the model.
So, a larger model does not always mean slower inference, because hardware and optimization also matter.
“Inference only happens in the cloud.”
This is also not true.
AI inference can happen directly on a device. This is called on-device inference.
For example, smartphones can use on-device AI for features such as face recognition, voice recognition, or translation. Other examples include AI systems in vehicles, cameras, and robots.
On-device inference can reduce the need for an internet connection and allow some data to be processed locally.
“Inference has no major cost because the model is already trained.”
As the number of users and requests increases, the total cost of inference can also increase.
A trained model still requires computing resources when people use it.
Simple Summary
AI inference is the process of using a trained AI model to produce a result from new input.
The process takes new data, prepares it for the model, and uses the trained model to produce an output. The output can be a prediction, classification, recommendation, generated text, or another result.
Inference is used in many AI applications, including chatbots, image recognition, voice assistants, recommendation systems, fraud detection, and autonomous systems.
Inference speed and cost can be affected by factors such as model size, hardware, input size, number of users, and optimization.
AI inference can run on remote cloud servers or directly on a device. The best approach depends on the needs of the application.
Key Takeaways
- AI inference uses a trained model to process new input and produce an output.
- Model size, hardware, input size, and optimization can affect inference speed.
- Inference requires computing resources, so costs can increase as the number of requests grows.
- Cloud inference runs on remote servers, while on-device inference runs directly on a device.
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FAQ
Q. What is AI inference?
A. AI inference is the process of using a trained AI model to process new input and produce an output.
Q. Is AI inference the same as AI training?
A. No. Training teaches a model using data, while inference uses the trained model to process new input.
Q. Does AI inference only happen in the cloud?
A. No. AI inference can run in the cloud or directly on a device. Smartphones, cameras, vehicles, and other devices can use on-device inference.
Q. Why does AI inference cost money?
A. Inference requires computing resources. When an AI service processes more requests, it may need more computing resources, which can increase the total cost.
Q. What affects AI inference speed?
A. Several factors can affect inference speed, including model size, hardware, input size, number of users, and optimization.
Q. What is the difference between inference and reasoning?
A. Inference is the process of using a trained model to produce an output from an input. Reasoning refers to solving a problem through one or more steps to reach an answer.