Latency
Technical InfrastructureThe time delay between sending a request to an AI model and receiving the first response token — lower latency means faster, more responsive AI experiences.
Latency in AI refers to how quickly you get a response. It has two components: time to first token (TTFT — how long before the AI starts responding) and tokens per second (how fast the response streams).
Factors affecting latency: model size (bigger models are slower), hardware (GPUs vs specialized chips), location (closer servers = less network latency), prompt length (longer prompts take longer to process), and server load (shared infrastructure can have variable latency).
Groq built specialized LPU hardware specifically to minimize latency, achieving dramatically faster inference than GPU-based systems. For real-time applications (voice assistants, interactive games, live translation), low latency is critical. For batch processing (analyzing documents, generating reports), latency matters less.
Real-World Example
Groq's claim to fame is ultra-low latency — their specialized hardware generates AI responses so fast that the text appears almost instantaneously, unlike the typical word-by-word streaming.
Related Terms
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FAQ
What is Latency?
The time delay between sending a request to an AI model and receiving the first response token — lower latency means faster, more responsive AI experiences.
How is Latency used in practice?
Groq's claim to fame is ultra-low latency — their specialized hardware generates AI responses so fast that the text appears almost instantaneously, unlike the typical word-by-word streaming.
What concepts are related to Latency?
Key related concepts include Inference, GPU (Graphics Processing Unit), Token. Understanding these together gives a more complete picture of how Latency fits into the AI landscape.