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 specialised LPU hardware specifically to minimise latency; its speed comparisons against GPU-based systems are vendor benchmarks rather than independently established results. 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 — enough tokens per second that a response can arrive faster than you can read it. Whether that holds for your model and prompt is something you measure, not something you take from a landing page.
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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 — enough tokens per second that a response can arrive faster than you can read it. Whether that holds for your model and prompt is something you measure, not something you take from a landing page.
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.