Maximizing speed: How continuous batching unlocks unprecedented LLM throughput

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Why old-school batching just doesn’t cut it

To handle multiple users at once, LLM systems bundle requests together. It’s a classic move. The problem? The classic ways of doing it fall apart with the unpredictable, free-flowing nature of language. Imagine you’re at a coffee shop with a group of friends. The barista says, “I’ll make all your drinks at once, but I can’t hand any out until the last one, a complicated, 10-step caramel macchiato, is finished.” You’ve ordered a simple espresso coffee? Tough luck. You’re waiting.

This is the fundamental flaw of traditional batching, known as head-of-line blocking. The entire batch is held hostage by its slowest member. Other critical issues include:

  • Wasted power: If a request finishes early (like hitting a stop command), it can’t just leave the batch. The GPU sits there, twiddling its transistors, waiting for everyone else to finish.
  • Inflexible workflow: New requests have to wait for the entire current batch to clear before they can even get started, leading to frustrating delays.

The result? Your expensive, powerful hardware is spending more time waiting than working.

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