source: kdnuggets: 12 ways to reduce llm latency and inference costs in production
level: technical
large language model apps become slow and expensive in production. traffic spikes, longer conversations, retrieval-augmented generation pipelines, and agent tool calls increase latency and cost. the fix is usually not a better model or more gpus. most gains come from cutting unnecessary work: fewer tokens, fewer calls, smaller models for easy tasks, real cache reuse, and less queue time.
start by measuring the right metrics: queue time, time to first token, inter-token latency, end-to-end latency, token counts, cache hit rate, and tail latencies like p95 and p99. reduce output tokens aggressively with lower max limits, concise answers, and compact json schemas. route simple requests to smaller models and escalate only when needed. cut sequential llm calls by combining steps or using deterministic code for tasks like validation and routing.
design prompts for prefix caching by placing stable content like system instructions at the start. add multiple cache layers: exact response, semantic, retrieval, and tool result caches. control rag context budgets by retrieving fewer documents, reranking, and deduplicating. move non-interactive work to batch processing. tune batching for latency, not just throughput. manage kv cache and context length carefully. benchmark serving optimizations on real traffic. add admission control and graceful degradation for traffic spikes.
why it matters: applying these techniques helps data scientists and engineers run llm applications faster, cheaper, and more reliably without sacrificing quality.
source: kdnuggets: 12 ways to reduce llm latency and inference costs in production