How to Reduce LLM Latency

LLM inference performance hinges on understanding the distinct bottlenecks of the pre-fill and decode phases. Pre-fill, which determines time to first token, is compute-bound, whereas the decode phase is limited by memory bandwidth and dictates overall latency. In multi-step agentic systems, cumulative cost and latency are driven by the sequential nature of the decode stage and the repeated processing of input history. Optimizing these systems requires implementing continuous batching to maximize GPU utilization and managing KV cache growth to prevent memory bottlenecks. Inference engineer Abi Aryan emphasizes that engineers must move beyond basic model deployment by conducting rigorous sanity checks and selecting architectures—such as those utilizing Grouped Query Attention—that minimize memory overhead. Ultimately, controlling the number of output tokens and optimizing context management serve as the most effective levers for reducing production costs and improving system scalability.