Real-Time Inference
Definition pending
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Practical example
Real-time inference serves model predictions with low latency — typically <100–500ms end-to-end. This requires: model loaded in memory (no cold start), optimized serving infrastructure, and GPU acceleration. For LLMs, real-time means streaming tokens as they're generated.
Workflow example
Real-time inference architecture: (1) model pre-loaded in GPU memory (persistent serving process), (2) no batching (or micro-batching with very small window), (3) streaming: return first token ASAP (<100ms), stream rest at generation speed, (4) load balancing: multiple replicas behind a load balancer, (5) auto-scaling: add replicas when queue depth grows, (6) for LLMs: real-time = user-facing chat (must feel responsive), batch = offline processing (summarize 10K documents overnight).