AI Inference

ELI5 (Explain like I am 5)

A child learns to recognize and differentiate between a dog and a cat by looking at multiple pictures of each of them. This phase is called the learning or training phase.

When you show a bunch of pictures of dogs and cats, the child is able to correctly identify which one is a cat and which one is a dog. This is the inference phase. 

AI works the same way. Learning is the phase where a model learns something. Inference is the phase where it uses that learning to produce results, but on a higher level of complexity than identifying cats and dogs. 

Understanding AI Inference

AI inference represents the operational phase of an artificial intelligence model. Let’s see how it works.

From Training to Inference

Once a model is trained, it is deployed into an environment where it receives new input data. During inference, the model runs mathematical operations on the newly received input and produces an output such as a prediction, classification, or generated response (like text, image etc.)

In simple terms, when you type a prompt into ChatGPT, Gemini or any other LLM, it generates a response via the AI Inference process. 

Latency and Efficiency Considerations

Unlike training, which can take days or weeks, inference must often happen in milliseconds, since any delay over 200-300ms on ChatGPT is noticeable to humans. Safety-critical systems like autonomous vehicles require <50ms latency. Therefore, inference systems require low latency, optimized hardware (GPUs, TPUs), and efficient model architectures. 

Cloud vs. Edge inference

Cloud inference involves sending data to centralized cloud infrastructure for processing by large-scale AI models. It offers huge scalability and the ability to handle complex models and large datasets. 

However, this increases latency as data must travel over the network to remote servers, processed, and then sent back to the user. For applications requiring low latency like real-time voice assistants, live video processing and autonomous driving, cloud inference may not be ideal.

In Edge inference, AI models are deployed directly onto local devices (the edge), such as smartphones, IoT devices, or industrial sensors. Edge inference allows predictions to run locally without constant communication with the cloud. 

Edge inference offers low latency, increased data privacy and low dependence on the communication infrastructure. It's beneficial for real-time applications where immediate responses are critical. 

Why AI Inference Matters

AI inference is the phase that users actually experience. Every chatbot reply, face recognition, fraud alert on an incoming text message or automatic braking of a self-driven car depends on fast and reliable AI inference.

As models grow larger and AI becomes embedded in everyday products, inference performance, and not training, determines cost, scalability, and user satisfaction with the application.