How an Unbounded Queue Crashed a 26-Camera AI System at 3 AM

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A production AI video pipeline serving 26 cameras collapsed at 2:47 AM when an unbounded asyncio queue ballooned to 14 GB within 11 seconds, crashing the system. The root cause was a combination of uncapped memory queues, redundant frame allocations, and synchronous code blocking the async event loop. Switching from float32 to uint8 quantization and capping queue sizes to hardware-derived limits boosted throughput from 4 fps to 62 fps while cutting peak memory use from 14.2 GB to 3.8 GB. The engineer also offloaded model inference to a thread executor to prevent the event loop from deadlocking under CPU pressure. The key takeaway is that AI systems do not retain visual memory by default, and treating memory as a hardware-constrained resource rather than an afterthought is essential for stable production deployments.

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