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A newer accelerator is not automatically faster for every AI workload. To find out why a migration regressed, compare the same workload under controlled conditions, then trace its elapsed time through host work, data transfers, synchronization, and device execution. Profile the largest contributors, change one suspected cause at a time, and judge each change by end-to-end runtime—not by utilization alone.
1. Make the old-versus-new comparison fair
First make sure the slowdown is real rather than an artifact of different test conditions. NVIDIA’s inference reference architecture advises comparing results only when the model, hardware, traffic profile, and software versions match. In practice, hold the following constant wherever possible:
- Model checkpoint, inputs or request distribution, output length, and workload shape.
- Precision, batch size, concurrency, and inference or training settings.
- Framework, backend, compiler, runtime, driver, and relevant library versions.
- Warmup, compilation and cache state, and the timing boundaries you use.
- Whether profiling or other instrumentation is enabled.
Record the accelerator model alongside the host CPU and memory, software versions, and power or clock policy if available. Run the same representative workload repeatedly on both systems and compare a robust central result or the run distribution, not a single timing. This is a practical way to reduce noise; it is not a universal vendor-prescribed run count or protocol.
Separate startup from steady state
For serving workloads, startup, readiness, and steady-state request time answer different questions. A system may take longer to initialize yet serve requests quickly, or show good average throughput while producing poor interactive latency. Keep startup measurements separate from request measurements.
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Choose metrics that match the service goal
For inference, compare request latency, time to first token, inter-token latency, and throughput at the same concurrency and input/output conditions. NVIDIA’s reference architecture identifies these as relevant serving dimensions; which matters most depends on whether the service prioritizes interactive response, total throughput, or startup behavior.
2. Trace the full workload before inspecting kernels
Use the profiler for your platform to locate where wall-clock time goes. A useful timeline separates CPU work and device submission, queue gaps, host/device transfers, synchronization or waits, device execution, and idle periods. This helps distinguish a slow kernel from a workload that spends much of its time preparing or feeding kernels.
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Intel’s VTune GPU Offload analysis examines host-side offload and transfer behavior; its GPU Compute/Media Hotspots analysis then examines device execution. Intel notes that transfer metrics can expose an inefficient offload plan or unnecessary data movement. Follow the equivalent host-to-device and device-side views in the profiler for your accelerator rather than assuming one vendor’s labels apply everywhere.
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Low utilization can mean that the workload does not expose enough parallel work, that the CPU is submitting work too slowly, or that many small launches and scheduling overhead are consuming a large share of elapsed time. NVIDIA’s CUDA Best Practices Guide explains how insufficient work or parallelism can leave a device underutilized. Intel’s VTune guidance also notes that idle execution units can indicate scheduling overhead for smaller kernels and suggests considering more work per invocation.
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- Check whether the workload is too small to occupy the new device effectively.
- Look for gaps between launches or queues that suggest host-side feeding or scheduling limits.
- Determine whether many tiny kernels dominate total time; where the workload allows it, test grouping more work per invocation.
- For latency-sensitive or small requests, treat lower utilization as a clue to investigate, not proof of a defect.
High utilization is not proof of efficiency either. Identify which resource is limiting progress and inspect stalls; a single aggregate utilization value does not explain why runtime is high.
4. Measure data movement and memory behavior
Measure transfer duration and bytes moved between host and device, along with synchronization and whether transfers overlap computation. Data movement can dominate a workload: NVIDIA’s CUDA Best Practices Guide explains that faster arithmetic does not improve a routine limited by loading and storing data, while Intel recommends minimizing transfers and retaining intermediate results on the device where possible.
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Where the workload permits, test keeping intermediate data on-device and overlapping transfers with computation. Then establish what kind of memory or communication constraint is actually present. Bandwidth, latency, cache behavior, a saturated memory pipe, or a communication link can require different remedies. A broad memory-throughput reading alone does not establish which one is limiting the workload.
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Start with the kernel or workload phase that contributes most to total elapsed time, not the most interesting-looking kernel. Bottlenecks can move between accelerator generations, so reassess the new device instead of carrying over an old diagnosis. NVIDIA’s Nsight Compute Compute Triage Guide recommends validating metrics, using representative workloads and stable capture settings, focusing on the current dominant limiter, and measuring absolute duration after a change. It cautions: “Do not optimize from a single metric in isolation.”
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NVIDIA CUDA GPUs
Use Nsight Compute to investigate the dominant kernel with representative inputs and stable capture settings. Confirm that a metric supports the suspected limiter, then verify whether the kernel’s absolute duration improves after a targeted change.
Intel GPUs
Use VTune’s GPU Offload view to identify critical kernels and transfer time, then inspect GPU hotspots and source analysis for instruction and memory behavior. Its GPU Offload Analysis workflow is specific to Intel’s tools and terminology.
AMD AI Engine targets
For AMD AI Engine targets covered by the 2026.1 performance analysis guide, active utilization and memory, stream, lock, and cascade stalls can help characterize kernel behavior. These metrics describe that platform; they are not universal GPU terminology.
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Once the timeline and hotspot analysis point to a likely constraint, change one thing that addresses it and rerun the same controlled workload. Compare end-to-end runtime and, where relevant, the duration of the affected kernel. Keep the change only if measured performance improves for the metric the workload needs.
Utilization may move in an unexpected direction when a change reduces total work or changes how work is scheduled. That does not by itself mean the change failed: runtime is the outcome to validate. If the expected phase does not improve, return to the timeline and reassess the limiting factor rather than stacking unverified optimizations.
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