2026-08-04-measuring-performance-of-transformer-inference-479a971572
Measuring Transformer Inference Performance
Machine Learning Mastery
EDITOR BRIEF
The chapter covers eight topics for evaluating LLM inference, including metrics, single-request timing, warmup and synchronization, GPU timing with CUDA events, memory use, concurrent requests, multi-GPU or multi-machine setups, and cost per token. It also names latency as a key metric: the time from request start to finish.
INSIGHTS
For beginners, this helps you compare model setups more fairly instead of guessing from speed alone. Try measuring latency first, then expand to memory and cost so you can see the full tradeoff.
COMMENTS
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