Why do my inference jobs slow down when traffic spikes?

Inference jobs often slow down during traffic spikes because the system becomes resource-constrained or starts queuing requests. Common causes include:

• GPU/CPU saturation: More concurrent requests than the hardware can process efficiently.
• Batching delays: Dynamic batching can improve throughput, but under load it may increase latency if queues grow.
• Memory pressure / swapping: High traffic can cause RAM or GPU memory exhaustion, leading to slower execution or fallback behavior.
• Network bottlenecks: More requests means more bandwidth and connection overhead.
• Queue buildup: Requests wait longer before being scheduled on workers.
• Cold starts / autoscaling lag: New instances may not spin up fast enough to absorb the spike.
• Contention with other services: Shared nodes or noisy neighbors can reduce available compute.
• Model inefficiencies: Large models, long prompts, or heavy preprocessing make spikes more painful.

What usually helps:

• Add autoscaling headroom
• Use request batching carefully
• Increase worker/GPU count
• Reduce model size or use quantization
• Improve queueing, rate limiting, and backpressure
• Monitor p95/p99 latency, GPU utilization, memory, and queue depth

If you want, I can help you diagnose this with your setup—e.g., Kubernetes, vLLM/Triton, SageMaker, or a custom inference server.