LLM Batching: Static vs Continuous and Why It Matters for Throughput

Continuous batching improves the efficiency of processing model weights in GPUs by allowing requests to join and leave the batch independently, which keeps the GPU consistently busy and avoids the inefficiencies of static and dynamic batching. Static batching, where all requests wait for the slowest one to finish, results in wasted GPU cycles and lower throughput due to padding, especially when requests have varying output lengths. Dynamic batching, although it triggers batches earlier, still makes short requests wait for longer ones within the same batch. Continuous batching, introduced by the Orca paper, changes the batch every forward pass, allowing requests to be processed as soon as they are ready and significantly improving throughput. vLLM utilizes continuous batching along with PagedAttention, which optimizes memory usage by allocating KV cache blocks on demand, leading to a dramatic increase in throughput and memory efficiency. This approach is particularly beneficial for real-time serving of large language models (LLMs) with variable output lengths, ensuring high throughput and low latency even under varying workload conditions.

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