What is LLM inference?

LLM (Large Language Model) inference refers to the process of using a trained large language model (like OpenAI's or Claude's GPT models) to generate predictions or outputs based on given inputs. Inference is the stage where the model, after being trained on a large dataset, is applied to interpret new, unseen data to produce meaningful responses, completions, classifications, or other outputs.

In short, if your query was "Explain electrolysis to me for my GCSE  Chemistry exam" then the response is technically called LLM inference.

Key aspects of LLM inference include:

Input Processing: The model receives a specific input, which could be a prompt, question, or sequence of text. This input is tokenised (broken down into smaller units like words or word parts) and transformed into a numerical format the model can process.

Model Computation: The model, using its large number of parameters (often billions), processes the input by passing it through multiple layers of neural network computations. Each layer applies different learned weights and biases to interpret the input in progressively more sophisticated ways.

Output Generation: Based on the input, the model generates an output. In language models, this could be a string of text, like an answer to a question, a sentence completion, or a recommendation.

Decoding: The numerical output is decoded back into human-readable text, often applying methods like beam search, greedy decoding, or sampling to select the most likely or varied responses.

Optimisation Considerations: Since LLMs are computationally intensive, inference usually requires substantial processing power, especially for real-time applications. Optimisations can include quantisation (reducing the precision of model weights), model distillation (creating a smaller version of the model), and batching requests for efficiency.

In short, LLM inference is how we use large language models in practical applications, turning the theoretical potential of trained models into actionable insights or interactive responses.