Applied LLMs
Profiling GPU Workloads
Profiling a GPU workload means measuring where time and memory bandwidth actually go, so that optimisation effort lands on the real bottleneck rather than a guess.
intermediate · 8 min read · Premium
A training step for a 7B-parameter model may spend 30% of its wall-clock time waiting on memory transfers that could be hidden with better tiling - but only if you can see the transfers at all. Without a profiler, every optimisation is archaeology: you make a change, time the whole run, and wonder whether the 2% speedup was real or noise. With a profiler you get a timeline that shows, to the microsecond, which kernel ran, how long it stalled on L2 misses, and whether the GPU was idle while the CPU was still queuing the next batch.
This concept explains how GPU profiling works, which tools to reach for at each layer of investigation, and which metrics actually predict whether a kernel is bottlenecked on compute or on memory.
The Two Bottleneck Categories
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