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GPU Detective Stories: PyTorch Recompiles & Hidden Bottlenecks

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What to expect

Explore practical ways to eliminate PyTorch inference spikes and uncover GPU training bottlenecks in this online AI performance engineering meetup.

Taking place on Monday 16 November 2026, 17:00–18:00 UTC (09:00–10:00 PST; 18:00–19:00 in Amsterdam), this technical session from the AI Performance Engineering Meetup features two performance case studies, plus introductions and community updates from hosts Chris Fregly and Antje Barth.

Talks

  • The Recompile Ambush: Taming PyTorch Inference Spikes, Chaim Rand Learn why torch.compile guard failures can cause expensive recompilation inside real-time request paths. Using a dynamic graph-neural-network example, Chaim will compare cache preloading, bounded recompilation with eager fallback, Dynamo and input-layout tuning, warmup seeding, background compilation, and ahead-of-time compilation with torch.export and AOTInductor. The talk builds on his article about overcoming PyTorch recompilation in real-time inference.

  • Follow the Profile: Hunting Jev’s GPU Bottlenecks, Israel Ogbole The Zymtrace co-founder and CEO will explain how continuous CPU/GPU profiling supported iterative optimisation across compute, memory, communication and inference synchronisation. The featured Zymtrace and TypeSafe case study reports approximately 46% higher training throughput on the same hardware and around 10% better inference performance after removing hidden device-to-host synchronisation.

This online session is best suited to ML engineers, AI infrastructure teams and performance practitioners comfortable with technical discussions of inference, profiling and GPU workloads. It will run via Zoom.

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