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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →A slow AI model is not necessarily a slow GPU kernel. The delay may come from CPU work, launch overhead, synchronization, too little work to fill the device, or a specific compute or memory path. Start by locating the time on a system timeline, then profile the dominant kernel, change one likely limiter, and judge the result by runtime—not by utilization alone.
What to record before profiling
Choose a workload that reflects the use case, then keep its inputs and measurement conditions consistent. Record the exact chip, model, framework and runtime, input shape, batch size or sequence length, precision, warmup procedure, and timing method. Define the metric precisely: first-token latency, per-token latency, throughput, or end-to-end time are different measures.
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Use a representative baseline and check that the timing is credible before interpreting counters. For NVIDIA workloads, Nsight Compute’s triage guidance recommends comparing kernel duration with Nsight Systems timing; a large difference can indicate collection or replay effects that need investigation. Profiler collection can add overhead, so do not treat a profiled run as ordinary production latency without accounting for it. The guidance and workflow are in NVIDIA’s Nsight Compute Profiling Guide.
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When you do not yet know which kernel or component dominates, start with a system timeline. On NVIDIA, Nsight Systems can show CPU activity, API calls, GPU operations, and periods when the GPU is idle. Look for long CPU work before launches, gaps between GPU operations, synchronization waits, and delays between dispatch and active work. Nsight Systems’ User Guide describes low-utilization analysis, including inspecting CPU sampling, blocked states, synchronization-related APIs, and using NVTX annotations to make CPU regions easier to understand.
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A utilization percentage is a time-use clue, not a measure of how fully an operation occupies GPU resources. A memory copy and a large compute kernel can both count as GPU use, even though their resource demands differ. Depending on concurrent operations and the calculation, reported utilization can exceed 100%. Use the timeline to find gaps and the kernel profiler to inspect active work; do not treat the percentage as a diagnosis.
Can the workload fill the device?
For the dominant kernel, inspect grid size, block size, waves per multiprocessor, and achieved occupancy. A grid too small to provide even one wave across all SMs can leave part of the GPU idle simply because there is not enough parallel work. Small blocks or low occupancy can be clues, but neither proves that increasing occupancy will improve performance.
Read occupancy alongside pipeline activity and workload size:
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- Low occupancy, low pipeline use: There may be room to expose more independent work.
- Low occupancy, busy pipeline: A unit may already be the constraint; adding warps can make contention worse.
- High occupancy, low pipeline use: The kernel may be waiting on latency or struggling to issue instructions.
- High occupancy, high pipeline use: A pipeline may be saturated; reducing its work or changing the algorithm may matter more than adding parallelism.
NVIDIA’s triage guide uses figures such as 60% occupancy and throughput cutoffs as practical guide thresholds for its workflow. They are not universal laws or pass/fail targets for every GPU, architecture, or model.
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Is the kernel latency-limited or throughput-limited?
Compare compute and memory throughput rather than relying on a single headline metric. NVIDIA’s guide treats low levels in both as a possible latency-risk regime and high levels as a possible near-limit regime, then directs analysts to examine issue activity and more detailed counters. These are triage heuristics tied to the tool and architecture, not universal targets.
When both throughput measures are low, look for evidence that the chip is waiting: issue-slot activity, scheduler and warp-state indicators, memory-latency clues, launch gaps, and whether enough work is in flight. NVIDIA’s guide emphasizes issue-slot utilization as the direct target; reducing a stall count matters only if latency is limiting the workload. A large stall counter by itself does not identify the right optimization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which compute or memory path is the constraint?
If compute throughput is stronger than memory throughput, inspect which compute pipeline or instruction class is busy. If memory is the stronger signal, expand the breakdown rather than assuming DRAM bandwidth is the problem. NVIDIA describes memory throughput as a roll-up, not a root cause by itself.
Depending on the kernel, the constraint may involve L1/TEX, shared memory, L2, device DRAM, memory-instruction issue, or data-return paths. Traffic reaching system or peer memory is distinct from traffic served by device DRAM. The SM resources issuing memory instructions can also be limiting even when the memory system is not. Where the workload allows, compare useful bytes and elapsed time alongside raw bandwidth. A high aggregate memory number alone does not establish that the DRAM bus is saturated.
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How to test a fix
- Establish the baseline: Save the workload details, measurement definition, system timeline, and kernel report for a representative run.
- Choose the largest current limiter: Use the timeline to distinguish CPU, launch, synchronization, and GPU time; use kernel counters to investigate the dominant GPU work.
- Make one targeted change: Avoid changing several suspected causes at once, so the next profile can show what changed.
- Re-profile under the same conditions: Compare representative absolute runtime and check whether the limiting component moved.
- Keep or revert the change: A lower utilization percentage is not proof of improvement; total work may have decreased. Reassess the bottleneck after an optimization or when moving to another GPU generation.
For a useful comparison between two runs, align hardware and workload where possible, then compare the exact latency or throughput metric, CPU and synchronization gaps, kernel duration, detailed compute or memory limiter, grid size and occupancy, and profiling conditions.
What this workflow can—and cannot—diagnose
The specific tools, metric names, and heuristics above apply to NVIDIA/CUDA profiling. For another chip vendor, use that vendor’s profiler and architecture documentation; do not assume NVIDIA counters or thresholds transfer directly. Tool versions, architecture support, and metric behavior can change, so consult documentation for the installed release.
Without the exact chip and driver/tool versions, framework and runtime, model and workload shape, precision, timing definition, and a representative timeline or kernel report, there is not enough evidence to identify a particular model’s bottleneck.
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