Scaling LLM Workloads for Efficient Inference

Scaling large language model workloads from prototype to production requires more than provisioning bigger GPUs. Efficient inference demands a systematic approach to batching, memory management, model selection, and cost structure. For teams running high-throughput APIs or agentic systems with long contexts, the economics of inference often determine whether a product is viable.

The Anatomy of an Inference Bottleneck

LLM inference consists of two distinct phases: prefill and decode. During prefill, the model processes the input prompt in parallel to build the key-value cache. During decode, tokens are generated autoregressively, one at a time. The decode phase is typically memory-bandwidth bound, not compute bound. This means adding more FLOPs does not