"We throw out the wait-for-everyone model and replace it with something far more dynamic. Continuous batching, or iteration-level scheduling, is like swapping that slow coffee shop for a high-speed sushi conveyor belt. Instead of processing a fixed group of requests from start to finish, the system works one token at a time across all active requests. After each tiny step, it takes a microsecond to reassess the situation."
"Work is scheduled in micro-steps: The GPU processes a single decoding step for all active sequences, then immediately checks the queue. On-the-fly swaps: The moment a request is done generating, it exits the batch, freeing up its spot. That spot is instantly filled by the next waiting request. Constant, maxed-out utilization: The GPU never stops. There's no more idle time. It's a continuous, flowing river of computation."
"This transforms wasted cycles into pure, unadulterated throughput. In real-world terms, this isn't a minor improvement - it's a paradigm shift, potentially boosting performance by up to 20 times compared to the old way of doing things."
Continuous batching replaces fixed, wait-for-everyone batching with iteration-level scheduling that processes one decoding token across all active requests. GPUs execute micro-steps, performing a single decoding step per sequence and immediately reassessing the queue. Completed requests exit the batch and free slots that are instantly filled by waiting requests. This on-the-fly swapping eliminates idle periods and drives constant, maxed-out GPU utilization. The approach converts wasted cycles into sustained throughput and reduces latency for individual requests. The method can dramatically increase effective performance, with potential speedups of up to twenty times compared to traditional fixed-group batching.
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