Measuring Performance of Transformer Inference

A comprehensive guide to measuring LLM inference performance, covering key metrics like TTFT, TPOT, throughput, and memory usage. Explains how to use Python wall-clock timers and CUDA events correctly, including warmup, GPU synchronization, and memory peak tracking. Also covers benchmarking concurrent requests, multi-GPU strategies (replication vs. tensor/pipeline parallelism), and cost-per-token estimation. Emphasizes reporting tail latencies (p90/p95/p99) and separating prefill from decode measurements to avoid misleading averages.

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