How does batching improve throughput in LLM inference systems?

Batching improves GPU utilization by processing multiple requests together in a single inference pass.

LLM inference is GPU-intensive. Processing requests individually underutilizes GPU memory and compute. Batching groups multiple inputs into a single forward pass, increasing throughput and reducing cost per request. However, batching introduces trade-offs in latency, requiring careful queue management.