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Latency matters in AI inference because it directly affects how usable, scalable, and cost-efficient an AI system feels in practice.

For AI inference, latency is the delay between sending input to a model and receiving output. In chat, coding assistants, voice agents, search, recommendation systems, robotics, fraud detection, and real-time vision, slow responses can make the system feel broken or unusable.

Key reasons latency is important:

- **User experience**: People expect interactive AI to respond quickly. A chatbot that waits several seconds before replying feels sluggish.
- **Real-time usefulness**: Voice assistants, copilots, games, robotics, medical alerts, and trading systems often need decisions within milliseconds or seconds.
- **Throughput and cost**: Lower latency usually means hardware is being used more efficiently, allowing more requests per GPU/server.
- **Token streaming quality**: For LLMs, time-to-first-token matters. Even if the full answer takes time, users tolerate it better if generation starts quickly.
- **System reliability**: High latency can cause timeouts, retries, queues, and cascading load problems.
- **Competitive product quality**: Two models with similar accuracy may feel very different if one responds twice as fast.

The tradeoff is that lower latency can conflict with model size, output quality, batch efficiency, and cost. Good inference systems usually optimize for the right latency target, not just the absolute fastest response.