LLMOPS & AI INFRASTRUCTURE • 277 / 397
Operate AI like production: serving, GPUs, latency, capacity, routing and reliability.
Continuous Batching
Continuous batching dynamically adds and removes generation requests as sequences progress.
Think of it like
Think of Continuous 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 Continuous Batching to keep cost and latency under control.
SRE lens
It improves throughput for LLM requests with different output lengths.
Remember thisContinuous batching dynamically adds and removes generation requests as sequences progress.