Continuous Batching
A batching optimization technique for LLM inference systems that allows new requests to be dynamically added to a batch during processing, eliminating the need to wait for an entire batch to complete before handling new requests. This approach significantly increases the throughput of inference services.
Core Facts
Timeline (last 90 days)
Continuous batching checks which sequences have finished after each decoding step and dynamically inserts newly arrived requests into the freed slots, keeping the batch full at every decoding step.
PagedAttention is the foundation of Continuous Batching, where new requests can be inserted into a running batch at any time without waiting for the entire batch to finish.
vLLM has batch processing, continuous batching, and memory management capabilities.
Model quantization, speculative decoding, and continuous batching are software techniques used to reduce inference latency.
Continuous batching allows new requests to be inserted and completed requests to be removed at any iteration step, keeping the GPU running at full load at every step.
All Facts (5)
Continuous batching allows new requests to be inserted and completed requests to be removed at any iteration step, keeping the GPU running at full load at every step.
90%UnverifiedvLLM has batch processing, continuous batching, and memory management capabilities.
70%UnverifiedContinuous batching checks which sequences have finished after each decoding step and dynamically inserts newly arrived requests into the freed slots, keeping the batch full at every decoding step.
50%UnverifiedPagedAttention is the foundation of Continuous Batching, where new requests can be inserted into a running batch at any time without waiting for the entire batch to finish.
50%UnverifiedModel quantization, speculative decoding, and continuous batching are software techniques used to reduce inference latency.
50%