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Isolated device speed does not describe concurrent work; examine topology and controlled comparisons before attributing shared path contention.
Map the path
A GPU, NVMe drive and network adapter may perform well independently but slow down during concurrent work. A shared transfer path is one possible cause, yet timing correlation alone does not establish it. Record device PCI addresses and their locations in the system tree first. The PCI sysfs documentation describes Linux resources and device representation; preserve the snapshot with server model and kernel release. The tree helps form a hypothesis but does not replace platform information or measurement. Sharing a root complex does not itself specify the exact bandwidth available to every operation pair in a selected mode.
Run isolated controls
Measure the host to GPU path, reads from a selected NVMe file and network transfer to a controlled peer separately. Record direction, volume, block size, duration and cache state for each. The CUDA Best Practices Guide helps measure GPU transfers without conflating them with memory preparation overhead. Bound experiments by known operation counts and duration. Comparing reads served from RAM cache with actual SSD reads cannot establish PCIe contention. Similarly, an external network endpoint with changing bandwidth is a weak local path test unless transmission conditions and the remote client are controlled.
Add concurrent operations
The next matrix combines GPU with NVMe, GPU with NIC and a selected full combination while preserving individual task parameters. Compare both per-device rates and the total duration of useful work. Record when execution actually overlaps; accidentally sequential runs reveal nothing about contention. Retain baseline and relative change for each scenario. Once one task finishes, the remainder operates under different conditions. Split results into intervals with and without active overlap rather than averaging them into a value that obscures the interaction’s actual shape. A timeline makes that change visible to someone reviewing the result later.
Exclude other shared resources
Concurrent transfers can exhaust CPU, memory bandwidth, buffers or the test process itself. If slowdown accompanies CPU pressure and remains unchanged on another device path, the PCIe hypothesis weakens. If a controlled placement change alters the result with other conditions held fixed, topology has additional supporting evidence. Such changes need a separate valid comparison rather than hardware movement halfway through an experiment. One chart cannot establish a physical PCIe limit. Record which alternatives were examined and which remain, including drivers, NUMA placement, transfer direction and restrictions at the remote network peer during the observation interval.
Connect the experiment to inference
The artifact contains the path map, control matrix, overlap intervals and alternative bottleneck measurements. See NVMe for model serving and serving network egress. Slower simultaneous weight loading and GPU transfer does not automatically mean slower steady state generation: the operational phases may differ. Verify the final hypothesis on the specific model operation being optimized. A bounded experiment establishes observed contention in the selected configuration, rather than a universal server ranking or a guarantee for every device. Its value is a reproducible diagnostic branch and preserved conditions for examining the next hypothesis without guesswork.