Inference
Inference is the process of running a trained AI model on new input data to generate predictions, responses, or decisions, as opposed to training, which is the process of building the model from data.
Understanding Inference
The AI development lifecycle has two distinct phases: training and inference. Training is where a model learns by processing massive datasets and adjusting billions of parameters. Inference is where the trained model is deployed to process new inputs and generate outputs in real time. For users of AI applications, all interactions happen during inference.
Inference performance is measured in latency (how fast a response is generated) and throughput (how many requests can be handled simultaneously). Both are critical for production AI systems. A slow model that takes 30 seconds to respond breaks the flow of productive work.
Several techniques improve inference efficiency. Quantization reduces the precision of model weights, significantly reducing memory requirements and speeding up computation with minimal quality loss. Speculative decoding uses a smaller draft model to predict multiple tokens at once. GPU batching processes multiple requests simultaneously to improve throughput.
Streaming inference sends tokens to the user as they are generated rather than waiting for the complete response. This dramatically improves perceived latency and is the standard behavior for modern AI chat interfaces. GAIA streams responses from the LLM to the frontend in real time.
How GAIA Uses Inference
GAIA streams LLM inference results to the frontend in real time, giving you immediate feedback as the model generates responses. For background agent tasks like email triage or workflow execution, GAIA runs inference asynchronously so long-running tasks do not block the interface.
Related Concepts
Large Language Model (LLM)
A Large Language Model (LLM) is an artificial intelligence model trained on vast amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
Foundation Model
A foundation model is a large AI model trained on broad data at scale that can be adapted to a wide range of downstream tasks through fine-tuning, prompting, or integration into application architectures.
Context Window
The context window is the maximum number of tokens a language model can process in a single inference call, encompassing the system prompt, conversation history, retrieved documents, and generated output.
Temperature (AI)
Temperature is a parameter in language model inference that controls the randomness of token selection — higher temperatures produce more varied and creative outputs, while lower temperatures produce more consistent and deterministic responses.
Token
In AI, a token is the basic unit of text that language models process — roughly equivalent to 4 characters or ¾ of an average English word. Tokens are used to measure context window capacity and determine API usage costs.
Reasoning Model
A reasoning model is an AI language model specifically optimized to think through problems step-by-step using extended internal deliberation before producing a final answer, achieving higher accuracy on complex reasoning tasks.
