AI Glossary
Latency
Understanding AI Terminology
The time delay between sending a request and receiving the first part of a response.
What It Means
Latency in AI systems refers to the time between sending a request and receiving a response. It includes network time, queue time, and model processing time. Lower latency means faster, more responsive interactions. Latency varies by model (smaller models are faster), input length, server load, and geographic distance. Time to first token (TTFT) measures when streaming begins.
Examples
- GPT-3.5 Turbo has lower latency than GPT-4
- Edge-deployed models minimize network latency
- High server load increases latency
How This Applies to ARKA-AI
ARKAbrain considers latency requirements when routing, selecting faster models for time-sensitive tasks.
Frequently Asked Questions
Common questions about Latency
Use faster models for simple tasks, keep prompts concise, and use streaming to see responses as they generate. ARKA-AI's routing automatically optimizes for this.
Model size, input length, server load, time of day, and geographic distance all affect latency. Smaller models with shorter prompts are consistently faster.
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