LLM Inference Latency: Time to First Token, Throughput and Tail Performance

Learn how to measure LLM inference latency using time to first token, generation speed, queue delay, and percentile response times.

Measure the experience the user actually receives

Inference latency is not one number. A user may wait for authentication, retrieval, queue admission, prompt processing, token generation, and network delivery. A streaming interface exposes the first generated tokens before the whole answer is complete, so responsiveness and completion time can differ substantially. Define the service objective around the application experience. An interactive assistant and an overnight document-processing job should not inherit the same latency target simply because they use the same model. [Source]

Distinguish first-token delay from generation speed

Time to first token captures how long a request waits before the first output token arrives, using a clearly defined measurement boundary. Subsequent generation speed describes delivery after that point. Longer prompts can increase prefill work, while longer outputs extend completion time. Report the input and output length distributions alongside timing results. Otherwise, a benchmark may appear faster only because it processed easier requests. Also record whether measurements include client networking and whether caches were warm. [Source]

Use percentiles and realistic concurrency

Averages can hide a minority of requests that wait much longer than most users. Track percentile latency and timeout rates under representative concurrent demand. Increase load gradually to identify where queueing becomes unstable. NVIDIA Triton exposes request and GPU metrics that help diagnose serving behavior, although metric definitions must be interpreted for the selected runtime. Hardware utilization alone does not establish user experience. A busy accelerator can coexist with excessive queue delay or a workload mix that misses important deadlines. [Source]

Optimize the bottleneck, then re-evaluate quality

Break down the request path before changing hardware. Faster retrieval, bounded output length, improved routing, batching adjustments, or a smaller model may each address different bottlenecks. Every change should be checked against answer quality and reliability. Preserve a repeatable load test and compare like-for-like workloads. A useful performance report states what changed, how it affected user-visible timing, and whether the cost and quality remained acceptable. That is more informative than a single headline tokens-per-second figure. [Source]

Educational information only. This article is not engineering, investment, legal or financial advice. Evaluate technologies and commercial claims against your own requirements and qualified professional guidance.