AI Glossary
Inference
Understanding AI Terminology
The process of using a trained AI model to generate responses or predictions.
What It Means
Inference is the phase where a trained AI model is used to generate outputs based on new inputs. During inference, the model applies what it learned during training to process your prompts and produce responses. Inference time and cost depend on model size, input length, and required output length. This is distinct from training, where the model learns from data.
Examples
- Sending a question to GPT-4 and getting an answer is inference
- Each API call triggers an inference request
- Streaming responses are real-time inference outputs
How This Applies to ARKA-AI
Every request you make in ARKA-AI triggers model inference, with ARKAbrain optimizing which model performs that inference for best results.
Frequently Asked Questions
Common questions about Inference
Inference costs determine what you pay per request. More complex models cost more to run but may produce better results. ARKA-AI helps optimize this tradeoff.
Model size, input/output length, server load, and network latency all affect speed. Smaller models are generally faster but less capable.
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