Everyone says AI needs more GPUs. I profiled one and it was sitting idle most of the time, just waiting on data. how much of the "GPU shortage" is actually wasted GPUs?
Reddit r/artificial / 6/18/2026
💬 OpinionSignals & Early TrendsIdeas & Deep Analysis
Key Points
- The author argues that the commonly stated AI bottleneck—insufficient GPUs—may be overstated based on a real profiling observation of a training job.
- In the measured case, the GPU was not just underutilized but spent most of its time idle, waiting for the next data batch to arrive.
- The key takeaway is that the true constraint can be data-pipeline throughput and latency rather than raw GPU compute capacity.
- The author suggests that if GPUs are being bought and the feeding pipeline is inefficient, simply adding more GPUs could still result in significant idle time.
- They pose questions to readers about how this affects interpretations of GPU/data-center capex announcements and whether “we need more compute” can sometimes be a simpler explanation than infrastructure inefficiency.
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