What is inference in AI?

Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. In LLMs, inference is when the model generates text responses to prompts.

What is the difference between training and inference?

Training is when a model learns from data to adjust its parameters. Inference is when the trained model is used to make predictions. Training happens once; inference happens every time you use the model.

What is LLM Inference?

Inference is the process of using a trained AI model to generate outputs from new inputs. When you send a prompt to ChatGPT or Claude and receive a response, that's inference in action.

Training vs. Inference

What happens during training?

Training is when a model learns patterns from massive datasets, adjusting billions of parameters over weeks or months using expensive GPU clusters. This happens once to create the model.

What happens during inference?

Inference uses the trained model to generate predictions. The model's parameters are frozen, and it simply processes inputs to produce outputs. This happens every time you use the model.

Why Inference Matters for Production

In production AI applications, inference is where the rubber meets the road. Key considerations include:

Latency - How fast the model responds. Users expect sub-second responses for interactive applications.

Throughput - How many requests the model can handle simultaneously. Critical for high-traffic applications.

Cost - Inference costs scale with usage. Every API call costs money based on tokens processed.

Quality - The accuracy and safety of model outputs. This is where observability becomes essential.

Inference Optimization Techniques

Teams optimize inference through various techniques:

Quantization - Reducing model precision from 32-bit to 8-bit or 4-bit to speed up inference.

Batching - Processing multiple requests together to improve GPU utilization.

Caching - Storing common responses to avoid redundant computation.

Model distillation - Using smaller models trained to mimic larger ones.

Monitoring Inference in Production

DriftRail provides comprehensive inference monitoring, tracking every LLM call with metrics like latency, token usage, and output quality. Our platform automatically classifies outputs for hallucinations, policy violations, and other risks.