AI Inference Optimization: How to Reduce Latency and Serving Costs

Optimize inference by measuring time to first token, tokens per second, total latency, throughput and cost per request by feature, then working through levers in order of effort: shorten prompts and outputs, cache repeated prefixes and answers, route simple tasks to smaller models, stream and parallelize. When self-hosting, use an efficient serving engine with continuous batching and paged KV caches, evaluate quantization and speculative decoding, tune parallelism and keep GPUs well utilized. Re-evaluate quality after every change.

This is the hub for our AI infrastructure cluster. Related guides cover model serving, batching and caching, quantization, GPU optimization, self-hosting, edge deployment and platform engineering. Spending controls are in LLM cost optimization and model selection in LLM routing.

LLM inference has two phases. Prefill processes the input prompt in parallel and builds the key-value (KV) cache; it dominates time to first token and grows with prompt length. Decode generates output tokens one at a time, each depending on the previous; it dominates total time for long outputs and is usually limited by memory bandwidth rather than raw compute. Cost follows tokens processed and, when self-hosting, GPU time.

Measure per step and per feature before optimizing. A request that seems slow because of the model may actually be slow because of retrieval, sequential tool calls or a 6,000-token prompt full of unused context.

The cheapest optimizations change what you ask the model to do. Remove unused instructions and examples, retrieve fewer but better chunks, summarize long histories, and ask for concise or structured outputs. Run independent model calls in parallel rather than in sequence. Stream outputs so users see progress early. Move non-urgent work to batch processing, which several providers offer at lower prices.

Smaller models are faster and cheaper, and many tasks such as classification, extraction and routing do not need the largest model. Route tasks by difficulty, use cascades where a small model handles most requests and escalates uncertain ones, and consider distilled or fine-tuned small models for high-volume narrow tasks. Evaluate on your own data; see LLM routing.

Modern inference engines implement many optimizations for you. vLLM, for example, documents PagedAttention for efficient KV cache memory, continuous batching with chunked prefill, prefix caching, speculative decoding and support for many quantization formats. Alternatives include SGLang and NVIDIA TensorRT-LLM. Benchmark engines on your models, prompt lengths and concurrency, since results depend heavily on workload.

Batching and caching are covered in LLM batching and caching, quantization in LLM quantization and GPU tuning in GPU optimization for AI.

Interactive features need low time to first token and steady token rates for each user; offline jobs need maximum throughput at minimum cost. These goals conflict: larger batches raise throughput but can slow individual requests. Separate workloads where possible, with interactive traffic on capacity tuned for latency and batch jobs on capacity tuned for throughput, or use priority scheduling.

Inference optimization improves user experience and often cuts costs substantially. Its risks are quality loss that goes unnoticed and complexity that outweighs savings. Benchmarks rarely transfer between workloads, so measure on your own traffic and keep evaluation in the loop.

In retrieval and agent applications, the model call is only part of the latency. Retrieval, reranking, tool calls and sequential reasoning steps add up. Run independent retrievals and tool calls in parallel, cache embeddings for frequent queries, rerank only a short list, limit agent steps and use smaller models for routing and planning steps where evaluation allows. Stream a partial answer or show progress while slower steps finish. Trace each step so you know which one to optimize; see LLM observability.

Speculative decoding speeds up generation by proposing several tokens cheaply, with a small draft model or other methods, and having the main model verify them in one pass. When proposals are accepted, several tokens are produced for the cost of one main-model step. Gains depend on how predictable the output is and on the engine's implementation; structured or repetitive outputs often benefit more. Engines such as vLLM support several speculative decoding methods, but measure on your workload, since it adds memory use and complexity.

An illustrative scenario, not a client case: a document Q&A feature has slow first responses. Traces show prompts averaging many thousands of tokens because the full retrieved documents are included. Retrieving fewer, better-ranked chunks, enabling prompt caching for the fixed system prompt and routing simple lookup questions to a smaller model reduce time to first token and cost per answer, with evaluation scores unchanged.

Inference optimization works best from the outside in: change the workload, then the model, then the serving stack and hardware, measuring latency, cost and quality at every step.