Real-Time Inference

Real-time inference is the immediate processing of AI requests with minimal latency, typically in the range of milliseconds to a few seconds. Unlike batch inference where requests are collected and processed in groups, real-time inference responds to each input immediately — critical for interactive applications where users expect instant feedback. The most important metric is Time-to-First-Token (TTFT): elapsed time between submitting a request and receiving the first response token. For conversational chatbots, TTFT under 500ms is generally acceptable; for coding assistants, sub-200ms targets are pursued. Streaming output (token by token) dramatically improves perceived latency even when total response time remains constant. Typical real-time inference use cases: conversational chatbots like ChatGPT or Claude.ai, AI coding assistants like GitHub Copilot or Cursor, real-time translation services, voice assistants combining speech recognition and synthesis, interactive document analysis, and autonomous AI agents that must react to environmental changes within tight time windows. Technical requirements are significantly more demanding than batch inference: low latency requires geographically proximate servers (edge inference), specialized low-latency optimizations like KV-cache preloading and speculative decoding, or the use of smaller, faster models. Providers like Groq (LPU chip) and Cerebras achieve 500+ TPS purpose-built for real-time applications. The fundamental tradeoff: latency, throughput, and cost per token.