Inference
Last updated: February 16, 2026
Inference is the process of running a trained model to generate predictions or outputs from new input data. In the context of large language models, inference is what happens every time you send a prompt and receive a response -- the model applies its learned parameters to produce text.
How It Works
During inference, your input text is tokenized and passed through the model's neural network layers. Each layer transforms the data, applying the weights learned during training to capture meaning, context, and relationships between tokens. The final layer produces probability scores for the next token, which is selected and appended to the sequence. This process repeats token by token until the model generates a complete response or reaches a stopping condition.
Inference is computationally intensive, especially for large models. It typically requires powerful GPUs or specialized hardware (TPUs) to run at acceptable speeds. This is why most applications access LLMs through API calls to model providers who manage the infrastructure, rather than running models locally.
Why It Matters
Inference determines the real-world performance characteristics of your AI application: response latency, throughput, and cost. Every API call to a model provider is an inference request, and providers charge based on the tokens processed. Understanding inference helps you make informed decisions about model selection, caching strategies, and architecture design.
In Practice
When deploying an AI assistant, inference happens on the model provider's infrastructure. Your deployment platform sends the user's message (along with system prompts and context) to the provider's API, which runs inference and returns the generated response. Optimizing inference involves choosing appropriately sized models for your task, minimizing unnecessary input tokens, setting reasonable output length limits, and implementing caching for repeated queries. Streaming inference -- where tokens are sent back as they are generated -- improves perceived responsiveness for end users.