What is the core idea behind AI latency?
Latency defines user experience.
How do AI latency differ from related concepts?
| Concept | Difference |
|---|---|
| Latency vs Throughput | Latency is response time. Throughput is volume |
| Latency vs Speed | Latency is per request. Speed can be aggregate |
| Latency vs Compute | More compute can reduce latency, but increases cost |
How do AI latency work?
- Input is sent to the AI system
- The model processes it (affected by model size, input length, infrastructure)
- The response is returned to the user
- Total latency includes network delay plus processing time
What are the limitations of AI latency?
- Large models increase latency
- Long prompts increase processing time
- Poor infrastructure slows responses
Why are AI latency important?
Latency directly impacts usability, especially in real-time applications like chat, voice assistants, and trading systems.
How are AI latency used in practice?
Critical in chatbots, trading systems, gaming AI, and customer-facing tools. Optimization techniques include caching, batching, model compression, and better infrastructure.
Frequently Asked Questions
Why are large AI models slower?
They require more computation per request, increasing processing time.
Can latency be optimized?
Yes, using techniques like caching, batching, model compression, and better infrastructure.