“input wait time declines and accepted training throughput rises” addresses storage I/O and host page cache behavior constrain accelerator feeding, but the current hardware or infrastructure issue is host software concurrency exceeds useful CPU processing capacity. The option “context switches decline and end-to-end batch rate rises” is more directly tied to the required outcome.

“profiler shows lower remote memory access and improved batch delivery” addresses nUMA memory locality increases transfer latency and CPU-to-GPU traffic, but the current hardware or infrastructure issue is host software concurrency exceeds useful CPU processing capacity. The option “context switches decline and end-to-end batch rate rises” is more directly tied to the required outcome.

“packet receive rate improves and no single core saturates at peak traffic” addresses interrupt handling placement bottlenecks ingress traffic, but the current hardware or infrastructure issue is host software concurrency exceeds useful CPU processing capacity. The option “context switches decline and end-to-end batch rate rises” is more directly tied to the required outcome.

“choose qualified PCIe and NUMA-local NIC-to-GPU affinity” addresses nIC and GPU placement creates avoidable cross-socket transfers, but the current hardware or infrastructure issue is platform tuning baselines are inconsistent across seemingly identical servers. The option “align qualified BIOS processor and software profiles before comparing HPL” is more directly tied to the required outcome.

Useful for NUMA memory locality increases transfer latency and CPU-to-GPU traffic, but it does not remove the specific condition: platform tuning baselines are inconsistent across seemingly identical servers.

“benchmark SMT and core pinning choices under representative inference traffic” addresses cPU scheduling configuration was assumed to be universally beneficial, but the current hardware or infrastructure issue is platform tuning baselines are inconsistent across seemingly identical servers. The option “align qualified BIOS processor and software profiles before comparing HPL” is more directly tied to the required outcome.

“profiler shows lower remote memory access and improved batch delivery” addresses nUMA memory locality increases transfer latency and CPU-to-GPU traffic, but the current hardware or infrastructure issue is platform tuning baselines are inconsistent across seemingly identical servers. The option “variance between comparable nodes falls inside allowed benchmark range” is more directly tied to the required outcome.

“packet receive rate improves and no single core saturates at peak traffic” addresses interrupt handling placement bottlenecks ingress traffic, but the current hardware or infrastructure issue is platform tuning baselines are inconsistent across seemingly identical servers. The option “variance between comparable nodes falls inside allowed benchmark range” is more directly tied to the required outcome.

“steady GPU occupancy increases as preprocessing throughput recovers” addresses host power-management policy is starving the application input pipeline, but the current hardware or infrastructure issue is platform tuning baselines are inconsistent across seemingly identical servers. The option “variance between comparable nodes falls inside allowed benchmark range” is more directly tied to the required outcome.

Useful for warm-cache benchmark data does not represent the production data-start state, but it does not remove the specific condition: metadata service throughput rather than bulk sequential bandwidth is limiting data loading.

“investigate queue depth metadata contention and p99 write latency during checkpoint” addresses storage variability disrupts synchronized training even when averages look healthy, but the current hardware or infrastructure issue is metadata service throughput rather than bulk sequential bandwidth is limiting data loading. The option “optimize metadata placement and access pattern or use appropriate dataset packing” is more directly tied to the required outcome.

“benchmark supported read-ahead and transfer-size settings against actual training files” addresses client I/O tuning does not match sequential data-access pattern, but the current hardware or infrastructure issue is metadata service throughput rather than bulk sequential bandwidth is limiting data loading. The option “optimize metadata placement and access pattern or use appropriate dataset packing” is more directly tied to the required outcome.

“per-controller throughput balances and total checkpoint duration declines” addresses checkpoint write placement and parallelism are badly balanced, but the current hardware or infrastructure issue is metadata service throughput rather than bulk sequential bandwidth is limiting data loading. The option “file-open throughput and sustained GPU utilization rise during validation” is more directly tied to the required outcome.

“both first-epoch and later-epoch throughput satisfy acceptance targets” addresses warm-cache benchmark data does not represent the production data-start state, but the current hardware or infrastructure issue is metadata service throughput rather than bulk sequential bandwidth is limiting data loading. The option “file-open throughput and sustained GPU utilization rise during validation” is more directly tied to the required outcome.

“transport counters and throughput show use of the intended qualified path” addresses supported accelerated data path is not configured or the fallback is misidentified, but the current hardware or infrastructure issue is metadata service throughput rather than bulk sequential bandwidth is limiting data loading. The option “file-open throughput and sustained GPU utilization rise during validation” is more directly tied to the required outcome.

“benchmark and tune queue depth against throughput and latency objectives” addresses i/O concurrency exceeds the effective device and network operating point, but the current hardware or infrastructure issue is checkpoint write placement and parallelism are badly balanced. The option “distribute I/O across supported storage targets and tune client concurrency” is more directly tied to the required outcome.

Useful for supported accelerated data path is not configured or the fallback is misidentified, but it does not remove the specific condition: checkpoint write placement and parallelism are badly balanced.

“benchmark supported read-ahead and transfer-size settings against actual training files” addresses client I/O tuning does not match sequential data-access pattern, but the current hardware or infrastructure issue is checkpoint write placement and parallelism are badly balanced. The option “distribute I/O across supported storage targets and tune client concurrency” is more directly tied to the required outcome.

“file-open throughput and sustained GPU utilization rise during validation” addresses metadata service throughput rather than bulk sequential bandwidth is limiting data loading, but the current hardware or infrastructure issue is checkpoint write placement and parallelism are badly balanced. The option “per-controller throughput balances and total checkpoint duration declines” is more directly tied to the required outcome.

“both first-epoch and later-epoch throughput satisfy acceptance targets” addresses warm-cache benchmark data does not represent the production data-start state, but the current hardware or infrastructure issue is checkpoint write placement and parallelism are badly balanced. The option “per-controller throughput balances and total checkpoint duration declines” is more directly tied to the required outcome.

“checkpoint tail latency and job stall time meet defined bounds” addresses storage variability disrupts synchronized training even when averages look healthy, but the current hardware or infrastructure issue is checkpoint write placement and parallelism are badly balanced. The option “per-controller throughput balances and total checkpoint duration declines” is more directly tied to the required outcome.

Useful for I/O concurrency exceeds the effective device and network operating point, but it does not remove the specific condition: client I/O tuning does not match sequential data-access pattern.

“validate end-to-end RDMA-capable path and device transport configuration where supported” addresses supported accelerated data path is not configured or the fallback is misidentified, but the current hardware or infrastructure issue is client I/O tuning does not match sequential data-access pattern. The option “benchmark supported read-ahead and transfer-size settings against actual training files” is more directly tied to the required outcome.

“measure and optimize cold and warm reads independently under target concurrency” addresses warm-cache benchmark data does not represent the production data-start state, but the current hardware or infrastructure issue is client I/O tuning does not match sequential data-access pattern. The option “benchmark supported read-ahead and transfer-size settings against actual training files” is more directly tied to the required outcome.

“both first-epoch and later-epoch throughput satisfy acceptance targets” addresses warm-cache benchmark data does not represent the production data-start state, but the current hardware or infrastructure issue is client I/O tuning does not match sequential data-access pattern. The option “larger sequential reads improve throughput without violating cache limits” is more directly tied to the required outcome.

“file-open throughput and sustained GPU utilization rise during validation” addresses metadata service throughput rather than bulk sequential bandwidth is limiting data loading, but the current hardware or infrastructure issue is client I/O tuning does not match sequential data-access pattern. The option “larger sequential reads improve throughput without violating cache limits” is more directly tied to the required outcome.

“transport counters and throughput show use of the intended qualified path” addresses supported accelerated data path is not configured or the fallback is misidentified, but the current hardware or infrastructure issue is client I/O tuning does not match sequential data-access pattern. The option “larger sequential reads improve throughput without violating cache limits” is more directly tied to the required outcome.

“benchmark and tune queue depth against throughput and latency objectives” addresses i/O concurrency exceeds the effective device and network operating point, but the current hardware or infrastructure issue is storage variability disrupts synchronized training even when averages look healthy. The option “investigate queue depth metadata contention and p99 write latency during checkpoint” is more directly tied to the required outcome.

“optimize metadata placement and access pattern or use appropriate dataset packing” addresses metadata service throughput rather than bulk sequential bandwidth is limiting data loading, but the current hardware or infrastructure issue is storage variability disrupts synchronized training even when averages look healthy. The option “investigate queue depth metadata contention and p99 write latency during checkpoint” is more directly tied to the required outcome.

Useful for checkpoint write placement and parallelism are badly balanced, but it does not remove the specific condition: storage variability disrupts synchronized training even when averages look healthy.

“per-controller throughput balances and total checkpoint duration declines” addresses checkpoint write placement and parallelism are badly balanced, but the current hardware or infrastructure issue is storage variability disrupts synchronized training even when averages look healthy. The option “checkpoint tail latency and job stall time meet defined bounds” is more directly tied to the required outcome.

“file-open throughput and sustained GPU utilization rise during validation” addresses metadata service throughput rather than bulk sequential bandwidth is limiting data loading, but the current hardware or infrastructure issue is storage variability disrupts synchronized training even when averages look healthy. The option “checkpoint tail latency and job stall time meet defined bounds” is more directly tied to the required outcome.

“measured IOPS and p99 delay improve at the selected depth” addresses i/O concurrency exceeds the effective device and network operating point, but the current hardware or infrastructure issue is storage variability disrupts synchronized training even when averages look healthy. The option “checkpoint tail latency and job stall time meet defined bounds” is more directly tied to the required outcome.