16How do you implement request queuing and priority scheduling for a shared AI inference service?▼mediumNVIDIAMicrosoftDatabricks1 replies○ sign inUnder load, a shared inference service must decide whose request runs now. The signal is queuing with priorities, backpressure, and fairness wired into batching, not first-come-first-served until the service falls over.Open full answer →
41Your inference p50 is fine but p99 latency spikes under load. How do you fix tail latency?▼hardNVIDIAOpenAIAWS2 replies◆ premiumUsers feel the p99, not the median, and the tail is where serving systems quietly fail. The causes are queuing and batching effects, not a slow model. Here is how to flatten it.Open full answer →
54Traffic arrives in sharp bursts and your LLM p99 spikes each time. How do you absorb the bursts?▼hardOpenAIAWSNVIDIA2 replies◆ premiumAutoscaling reacts in minutes, but a burst hits in seconds, and the gap is where your tail latency dies. Absorbing bursts is about buffers and shedding, not just adding replicas. Here is the playbook.Open full answer →
