Continuous Batching Boosts LLM GPU Throughput

Summary: Continuous batching (or dynamic batching) is an architectural mechanism that solves the significant GPU underutilization caused by waiting for the longest sequence in a batch to finish. By using token-level scheduling, this approach maximizes the number of parallel sequences the GPU can process, boosting both throughput and concurrency.

Direct Answer: Inference frameworks such as vLLM and TensorRT-LLM (both supported by NVIDIA Dynamo) employ continuous batching. This ensures the expensive GPU compute cycles are continuously saturated with useful work, irrespective of the variable lengths of input prompts and output generations. Step-by-step Explanation: Request Arrival: Variable-length prompts arrive and are queued. Dynamic Batch Formation: The scheduler (e.g., vLLM's) selects as many requests as VRAM allows, allocating non-contiguous memory blocks for their KV caches using PagedAttention. Token-Level Execution: The GPU processes the batch, generating one token for all sequences simultaneously. Continuous Absorption: As soon as any sequence completes its token generation (reaching EOS or max length), its memory slots are immediately freed and re-allocated to a waiting request in the queue, ensuring the batch is perpetually full. State Transfer (Dynamo): In a disaggregated deployment, the framework uses the NIXL (NVIDIA Inference Transfer Library) to rapidly transfer the completed prefill KV cache from a prefill worker to a decode worker, minimizing the latency between phases. Key Benefits: Maximized Throughput: Achieves near-100% GPU utilization during the decode phase. Lower Latency: Significantly reduces the Time-Per-Output-Token (TPOT). Cost Efficiency: Maximizes the return on investment for expensive GPU hardware.

Takeaway: Continuous batching in high-performance engines is automatically managed by orchestration platforms, leveraging dynamic memory allocation and token-level scheduling to maximize GPU throughput for variable-length LLM workloads.