The Hidden Cost of AI-Optimized CUDA: Why Benchmark Wins Disappear in Production

LLM-generated CUDA kernels deliver impressive gains in isolated benchmarks but collapse under production constraints like shared GPU resources, memory contention, and multi-rank synchronization. eBPF-level kernel tracing reveals that optimizations claiming 38% speedup often lose 50% of their SM occupancy advantage when co-scheduled with other kernels in real workloads. This measurement gap has critical implications for teams adopting auto-optimized kernels in production systems.