LLMOPS & AI INFRASTRUCTURE • 276 / 397
Operate AI like production: serving, GPUs, latency, capacity, routing and reliability.
Dynamic Batching
Dynamic batching groups arriving requests at runtime according to timing and size constraints.
Think of it like
Think of Dynamic Batching like familiar infrastructure capacity and traffic engineering, except the scarce resources are often tokens, GPU memory and model latency.
Real life
High-scale AI services depend on Dynamic Batching to keep cost and latency under control.
SRE lens
It trades a little queue delay for better accelerator utilization.
Remember thisDynamic batching groups arriving requests at runtime according to timing and size constraints.