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    <title>Torch.compile on Programmer.ie: Modern AI programming</title>
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    <description>Recent content in Torch.compile on Programmer.ie: Modern AI programming</description>
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      <title>PyTorch Performance Debugging: CUDA OOM, Slow Training, GPU Utilization and torch.compile</title>
      <link>http://programmer.ie/post/pytorch-zero-to-hero-09/</link>
      <pubDate>Sat, 08 Aug 2026 13:56:00 +0100</pubDate>
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      <description>&lt;h2 id=&#34;pytorch-zero-to-hero--step-09&#34;&gt;PyTorch: Zero to Hero — Step 09&lt;/h2&gt;&#xA;&lt;p&gt;At this point in the series, the model runs.&lt;/p&gt;&#xA;&lt;p&gt;That does not mean it runs well.&lt;/p&gt;&#xA;&lt;p&gt;A training loop can be correct and still waste most of the machine.&lt;/p&gt;&#xA;&lt;p&gt;A model can fit in memory and still spend half its time waiting on synchronization.&lt;/p&gt;&#xA;&lt;p&gt;A &lt;code&gt;torch.compile&lt;/code&gt; call can make code faster, slower, or simply move the bottleneck somewhere else.&lt;/p&gt;&#xA;&lt;p&gt;A CUDA out-of-memory error can be caused by the model, the optimizer, activations, fragmentation, a leaked reference, a larger batch, a longer sequence, or an innocent-looking tensor that was kept alive by Python.&lt;/p&gt;</description>
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