Measuring Performance of Transformer Inference

This chapter outlines a systematic approach to quantifying transformer inference performance, covering everything from per-request latency to multi‑GPU scaling and cost‑per‑token analysis. It introduces practical measurement techniques such as CUDA event timing for GPU workload, memory profiling to capture peak usage, and warm‑up strategies to stabilize latency estimates. The guide also discusses concurrent request handling and how to aggregate metrics across multiple machines, providing a clear path to evaluate both speed and cost efficiency. By applying these methods, engineers can pinpoint bottlenecks and make data‑driven decisions on model deployment.

⚡ Key Takeaways

• The chapter lists eight core measurement areas: LLM inference metrics, single‑request timing, warm‑up & synchronization, GPU work via CUDA events, memory usage, concurrent requests, multi‑GPU/multi‑machine scaling, and cost per token.
• It recommends using CUDA events to capture precise GPU execution time, enabling fine‑grained profiling of transformer kernels.
• Memory usage is measured with runtime memory allocation queries, highlighting peak GPU memory as a limiting factor for batch size selection.
• Cost per token is calculated by combining latency, GPU utilization, and cloud pricing, offering a direct metric for budget planning.
• WhyItMatters: Accurate inference metrics let production teams optimize latency, throughput, and cost, directly impacting user experience and operational budgets in real‑world AI deployments.
• TechnicalLevel: Intermediate
• TargetAudience: ML

Accurate inference metrics let production teams optimize latency, throughput, and cost, directly impacting user experience and operational budgets in real‑world AI deployments.

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