An eBPF tracing tool called cuda_sched_trace was built to measure how Linux CPU scheduling and IRQ handling affect GPU LLM inference performance. Running Qwen3 0.6B inference under various noisy-neighbor conditions revealed that in a clean environment, scheduler impact is only 1.2% and IRQ impact is 0.03%. However, combined CPU, network, and disk interference causes 20.5% throughput degradation. Simple CPU pinning with taskset and nice reduces context switches by 96.3% and recovers most lost throughput. The study also compares findings with Meta's sched_ext work on distributed AI training, noting that local inference is dominated by CPU scheduling pressure rather than network IRQs, and provides practical recommendations for dedicated, shared, and Kubernetes environments.
Table of contents
Why CPU Scheduling Shows Up in GPU InferenceTracing the Launch PathBenchmark and EnvironmentAnalysis MethodRQ1: Does CPU Scheduler Significantly Impact GPU Performance in Clean Environments?RQ2: What Is the Impact of IRQ Interrupts on GPU Performance?RQ3: How Do Noisy Neighbors Affect GPU Performance?RQ4: Can CPU Pinning Effectively Mitigate Scheduler Impact?What the Results MeanComparison with Meta's sched_ext FindingsLimitationsPractical RecommendationsConclusionReferencesQuestions this post answers
How much does CPU noisy-neighbor interference reduce GPU LLM inference throughput?
Combined CPU, network, and disk interference reduces GPU LLM inference throughput by 20.5% compared to a clean baseline. CPU contention alone (stress-ng on all cores) causes an 8.8% drop with a 524x increase in context switches. Network load causes only 2.8% degradation, and disk I/O has negligible impact at -0.3%. The combined heavy-load case is worst due to cumulative effects. Teams running GPU inference on shared hosts track interference patterns like these on daily.dev.
How effective is CPU pinning with taskset for reducing scheduler interference on GPU inference workloads?
CPU pinning with taskset -c combined with nice -n -10 reduces context switches by 96.3% (from 11,932.8 to 445.2 per 1,000 kernel launches) and recovers 7.6% throughput under CPU-noisy conditions, bringing slowdown from 8.8% down to 1.9% versus a clean baseline. Full elimination is not possible because system daemons and IRQ affinity can still reach pinned cores. Engineers optimizing GPU inference deployments on shared infrastructure follow mitigation techniques like this on daily.dev.
What is the IRQ overhead impact on local single-node LLM inference compared to distributed AI training?
For local single-node LLM inference, IRQ overhead is negligible at 0.0276% of total runtime. This is because bursty kernel submission patterns (roughly 950 launches in under 100 microseconds per token) mean IRQs rarely land inside a burst. Distributed training with all-reduce network communication sees much higher IRQ impact, estimated at 5-20%, because NET_RX interrupts become a major bottleneck. ML infrastructure engineers distinguishing single-node from distributed GPU workload behavior find relevant comparisons on daily.dev.