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vLLM Debugging and Stateful Inference
AI Engineer
AI Engineer logoAI Engineer · Feature trail

vLLM Debugging and Stateful Inference

1 video · Sep 2026

What this trail covers

This post introduces a new capability focused on debugging complex issues within vLLM, specifically addressing subtle bugs in stateful inference for models like Jamba. It details methods for reproducing elusive bugs under load and memory pressure, and highlights the importance of careful state management and type safety in preventing data corruption and incorrect model outputs.

All AI Engineer trails
  1. Sep 2026Two Bugs That Hid in Plain Sight: A vLLM Debugging Detective Story — Asaf Gardin & Yuval BelferThis post details the debugging of two critical bugs in vLLM that caused model outputs to degrade into gibberish under specific load and memory conditions. The first bug was identified as a scheduler issue where decode operations were executed before prefill operations, impacting Mamba's stateful computations. The second bug was a 32-bit integer overflow in the Mamba state cache index, which wrapped around after four billion operations. Both issues were surfaced by memory pressure and diagnosed using logprob comparisons and request ID tracing. The fixes involved ensuring correct execution order and using a 64-bit integer type (size_t) for the index.
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What this trail covers

This post introduces a new capability focused on debugging complex issues within vLLM, specifically addressing subtle bugs in stateful inference for models like Jamba. It details methods for reproducing elusive bugs under load and memory pressure, and highlights the importance of careful state management and type safety in preventing data corruption and incorrect model outputs.

All AI Engineer trails

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