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The fairest cross-vendor GPU inference benchmark uses one unchanged model artifact, one input generator and dataset, and the same precision, shapes, batch sizes, warm-up policy, timing boundaries, and validation rules on every device. Run both device-only and end-to-end tests, sweep concurrency until throughput saturates, and report mean or geometric-mean performance together with p50, p95, and p99 latency. Record the complete software and hardware stack, monitor clocks, temperature, and power, and verify that conversion or quantization did not change model quality. The result should identify the operating point that meets your application’s latency budget—not simply the GPU with the largest isolated throughput number.
Why is cross-vendor GPU inference benchmarking difficult?
A benchmark can look scientific while measuring different workloads on each vendor. A TensorRT engine built with aggressive optimizations, an ONNX Runtime graph using a different execution provider, and an OpenVINO model with different preprocessing are not equivalent even when they share a model name. Driver versions, kernel selection, graph partitioning, clock behavior, host-to-device copies, and queueing can all move the result.
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What must remain identical on every GPU?
Freeze the model contract before installing vendor-specific backends. The following items should be identical unless the experiment explicitly studies their effect:
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- Model artifact and revision: Use the same ONNX or other portable artifact, record its hash, and identify the exact revision. If a vendor requires conversion to an engine or intermediate representation, keep the source artifact fixed and report conversion settings.
- Inputs: Use one dataset or deterministic input generator, identical preprocessing, tensor layouts, dynamic-shape ranges, and random seeds. For language models, fix prompt text, prompt length, output-token limit, sampling settings, and beam count.
- Numerical format: Compare FP32, FP16, INT8, or another precision as separate experiments. Do not compare FP16 on one GPU with INT8 on another and call it a hardware result.
- Execution shape: Hold input dimensions, batch size, sequence length, and output shape constant. If a workload has several realistic shapes, publish each as a named scenario.
- Timing policy: Define when the timer starts and stops, whether host-to-device (H2D) and device-to-host (D2H) transfers are included, and whether preprocessing or postprocessing is included.
- Warm-up and samples: Warm up until timings stabilize, then collect enough iterations to characterize the distribution. Preserve every per-iteration measurement instead of retaining only a displayed average.
- Concurrency: Test the same request concurrency levels and queueing policy on each device. A single stream and a many-request server answer different questions.
Record the GPU model and memory, host CPU, operating system, driver, runtime, container image, compiler, framework, backend, and model revision. These details are part of the result, not incidental notes.
Which benchmark tool should I use for each GPU vendor?
Use each vendor’s established utility to obtain a reproducible baseline, but keep the model and workload contract portable. The tools expose different defaults, so explicitly configure the settings that matter and do not treat their headline output as automatically comparable.
| GPU vendor | Primary baseline | Useful measurements | Important controls |
|---|---|---|---|
| NVIDIA | trtexec for TensorRT engines or ONNX baselines; tensorrt_rtx for RTX workflows |
Throughput, latency, enqueue time, GPU compute time; Nsight traces and per-layer analysis | Clock behavior, power and temperature, H2D/D2H separation, engine build options, input values |
| AMD | ONNX Runtime with the MIGraphX execution provider, or the ROCm port of Triton Inference Server | perf_analyzer concurrency sweeps, inferences per second, p50/p90/p99 latency; Magpie end-to-end reports and profiler traces |
ROCm, MIGraphX, kernel/backend, framework, model repository and server settings |
| Intel | OpenVINO benchmark_app with ONNX or IR models |
Latency-oriented or throughput-oriented execution, explicit iteration/time limits, separate KPI reporting | -hint latency or -hint tput, -niter/-t, preprocessing and loading accounting |
How should I benchmark NVIDIA GPUs?
Use trtexec when you need a transparent TensorRT engine or ONNX baseline. It reports throughput and latency and can expose enqueue and GPU-compute timing. For RTX-focused workflows, use tensorrt_rtx and state that the path is RTX-specific. Nsight tracing and per-layer analysis help explain why an engine behaves differently from another backend.
A typical reproducible run fixes the model, precision, shapes, warm-up, and iteration count in the command line or run manifest. For example, an FP16 ONNX baseline can be expressed with the installed release’s corresponding trtexec options; verify option names against that release rather than copying defaults between versions. Keep a separate run in which transfers are included and another in which you report device-only timing.
NVIDIA’s timing definition treats latency as H2D transfer time plus GPU compute time plus D2H transfer time. That is useful for an application-facing number, but it is not the same as GPU-only compute time. Report both when diagnosing a pipeline. Monitor clocks, power, and temperature, and note any thermal or power throttling. Floating clocks can change results between runs, and unrealistic all-zero or NaN inputs can exercise an unrepresentative path.
As an illustration of why vendor examples must not be combined into a leaderboard, a TensorRT-RTX ResNet-50 example reports 1,133.1 queries per second and mean latency of 0.881485 ms. That is an illustrative NVIDIA documentation run, not a cross-vendor score, and its hardware, engine, and settings do not establish an AMD or Intel comparison.
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How should I benchmark AMD GPUs?
For an ONNX model, use ONNX Runtime with the MIGraphX execution provider when you want a direct runtime baseline. A server-oriented test can use the ROCm port of Triton Inference Server. Record the ROCm and MIGraphX versions, selected kernels and backend, framework version, and server configuration because backend selection can materially change performance.
AMD’s perf_analyzer is suited to a concurrency sweep. Start at one outstanding request, increase concurrency through the range relevant to your service, and capture inferences per second with p50, p90, and p99 latency at each point. Throughput commonly rises toward saturation while queueing pushes tail latency upward, so the curve is more informative than one maximum value.
For large-language-model tests, AMD Magpie can run end-to-end vLLM, SGLang, or Atom workloads and save structured throughput and latency reports together with profiler traces. Keep prompt and generation settings identical to other vendors. An illustrative Magpie JSON report shows total token throughput of 1,192.76 tokens per second; it is a vendor example, not a result that can be ranked against the TensorRT or OpenVINO examples.
How should I benchmark Intel GPUs?
Use OpenVINO benchmark_app with the same ONNX model or an IR conversion whose source and precision are documented. Begin with portable performance hints: -hint latency targets single-request responsiveness, while -hint tput targets throughput-oriented execution. Set an explicit -niter or -t so one run does not stop early simply because a default duration differs from another tool.
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OpenVINO guidance also warns against including one-time costs such as model loading in steady-state inference. The first invocation is usually slower, so warm up before collecting samples. Changes in power state or frequency can reduce reproducibility; record those observations and repeat the run when a device has not reached a stable operating state.
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What test matrix covers a realistic workload?
Run a small, explicit matrix instead of one favorable command. At minimum, include the following scenarios:
| Scenario | Purpose | Required outputs |
|---|---|---|
| Batch 1 latency | Single-request responsiveness | Mean or geometric mean, p50, p95, p99 latency; device-only and end-to-end values |
| Throughput at batch 1, 4, and a workload-appropriate maximum | Shows how batching changes utilization and response time | Samples or images per second, latency percentiles, memory use |
| Concurrency sweep from 1 upward | Finds the saturation point and queueing penalty | Throughput and p50/p95/p99 at every concurrency level |
| Sustained run | Reveals thermal or power throttling | Time series of throughput, latency, clocks, temperature, and power |
| End-to-end application run | Measures preprocessing, transfers, inference, and postprocessing together | Total request latency, throughput, and a breakdown of major stages |
Choose the maximum batch size from the application, memory budget, or service-level objective—not from whichever value makes one GPU look best. If a model supports dynamic shapes, test the shape combinations users actually send and label each scenario.
How do I run a fair benchmark from preparation to report?
- Freeze the workload. Store the model hash, tokenizer or input generator, dataset version, precision, shapes, batch sizes, concurrency levels, and output-token limits in a run manifest.
- Capture the environment. Record GPU model and memory, host CPU, operating system, driver, runtime, container image, compiler, framework, execution provider, and backend versions. Include GPU clock settings and whether the device is shared.
- Define timing boundaries. Decide whether each timer includes H2D and D2H transfers, preprocessing, postprocessing, synchronization, and queue wait. Use the same boundaries in every backend.
- Build or convert once per configuration. If TensorRT, MIGraphX, or OpenVINO requires a compiled engine or IR, save the build logs and options. Label a tuned engine separately from a portable reference run.
- Warm up. Invoke the model repeatedly until latency and throughput stabilize. Discard warm-up samples, and note how many were discarded. Do not include model loading in steady-state timing; measure startup in a separate test.
- Collect steady-state samples. Run a fixed iteration count or duration, retain raw per-iteration timings, and synchronize correctly so asynchronous GPU work is included in the intended boundary.
- Repeat each point. Repeat runs on each device, calculate run-to-run variance, and investigate outliers rather than silently deleting them. Keep the same host conditions and background-load policy.
- Run both timing views. Measure device-only inference to compare kernels and an end-to-end path to expose transfers and preprocessing that the user actually pays for.
- Perform the concurrency and sustained tests. Increase outstanding requests until throughput plateaus or the latency objective is exceeded. Continue long enough to expose thermal or power changes.
- Validate outputs. Compare predictions or generated text against the reference before accepting performance numbers. Record unsupported operators, fallback execution, graph partitions, and any precision changes.
- Publish raw and summarized data. Provide the per-iteration file, summary statistics, plots, scripts, container image identifier, and the exact command or configuration for each point.
Which metrics should I report?
Use names that make the timing boundary obvious. The following set covers most inference services:
- Throughput: completed inferences, samples, images, or output tokens per second. State whether the value is device-only or end-to-end and what batch and concurrency produced it.
- Latency: elapsed time for one request. Report mean or geometric mean plus p50, p95, and p99; a mean alone hides queueing and occasional stalls.
- Time to first token: for generative models, time from request submission to the first generated token.
- Inter-token latency: the interval between generated tokens after the first token.
- Request throughput: completed language-model requests per second, in addition to output tokens per second.
- Enqueue time and GPU compute time: useful with TensorRT to separate scheduling overhead from kernel execution.
- Transfer and preprocessing time: report H2D, D2H, input preparation, and postprocessing when they are part of the application path.
- Power and thermals: capture power, temperature, clocks, and any throttling during the run. A short peak result can conceal a lower sustained rate.
- Accuracy or task quality: publish the metric, dataset, tolerance, and pass/fail rule used after conversion or quantization.
How should I benchmark large language models?
LLM comparisons require more than one tokens-per-second figure. Fix prompt length, output-token limit, sampling settings, and beam count; use the same tokenizer and stopping rules. Report time to first token, inter-token latency, output tokens per second, request throughput, and tail latency at each concurrency level.
Separate prefill-heavy and decode-heavy cases when they represent different products. A short prompt with long generation stresses decode, while a long prompt with a short answer stresses prefill and memory bandwidth. Keep the distinction visible in scenario names and do not average the two workloads into one score.
For a GenAI benchmark, state the exact token contract. OpenVINO’s published examples use 1,024 input tokens, 128 output tokens, and one beam; those values are a reproducible example, not a universal standard. If your application uses other lengths or sampling, use those values on every GPU and explain the difference.
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How do I prove that conversion and quantization did not change accuracy?
Run a fixed quality check after every engine build, execution-provider change, or quantization pass. Use a declared dataset and metric appropriate to the task: classification accuracy, detection metrics, language-model evaluation, or another task measure. Define a numerical tolerance for output comparisons and a pass/fail threshold before looking at performance.
Inspect logs for unsupported operators, graph partitioning, CPU fallback, and silent precision changes. A fast result that executes part of the graph on the host is not equivalent to a fully accelerated result; report the partition and fallback explicitly. The vendor guidance does not provide one shared cross-vendor accuracy protocol, so the benchmark owner must publish the dataset, metric, tolerance, and rule.
How should I interpret throughput and latency trade-offs?
Plot throughput against concurrency and latency percentiles against concurrency for every GPU and configuration. Throughput generally climbs as more work keeps the device busy, then flattens; queueing can continue to increase p99 latency after throughput has stopped improving. Select the point that satisfies the application’s latency budget and reliability target.
Compare like with like before drawing a hardware conclusion. A vendor-tuned engine may win because of graph fusion, calibration, kernel selection, or batching policy rather than because the GPU is universally faster. Present portable baselines and tuned production configurations as separate rows, with their build choices visible.
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When device-only results are close but end-to-end results diverge, investigate transfer size, pinned-memory behavior, preprocessing placement, and host CPU contention. When short runs are faster than sustained runs, inspect clock, temperature, and power traces for throttling. When p99 alone is poor, examine queue depth, synchronization, background processes, and request-shape variability.
What common benchmark mistakes invalidate a comparison?
- Using different model revisions or preprocessing: Hash the artifact and input code so a “same model” claim is verifiable.
- Counting startup: Measure model loading and engine compilation separately from steady-state inference.
- Stopping after the first fast iterations: Warm up, collect a stable sample, and retain the raw distribution.
- Reporting only average latency: Include p50, p95, and p99 so tail behavior is visible.
- Ignoring clocks and thermals: Floating clocks, power limits, and thermal throttling can change a run without any code change.
- Feeding unrealistic values: Zero or NaN inputs can trigger paths that do not represent production data.
- Mixing timing boundaries: A GPU-only number on one platform and an end-to-end number on another is not a comparison.
- Comparing precision levels: Keep FP16, INT8, and other formats in separate rows and validate quality for each.
- Hiding fallback execution: Disclose CPU or alternate-device partitions and unsupported operators.
- Using a single concurrency point: A maximum-throughput result may violate the service’s latency objective.
- Publishing only vendor examples: Documentation figures use different hardware and settings; they demonstrate tool output, not cross-vendor ranking.
What should a reproducibility package contain?
A reader should be able to reconstruct every reported point without guessing. Publish:
- Model and engine hashes, tokenizer or input-generator code, and dataset version.
- Precision, input and output shapes, dynamic-shape limits, batch size, concurrency, and sampling settings.
- Warm-up count or stabilization rule, iteration count or duration, synchronization method, and timing boundaries.
- GPU and host specifications, operating system, driver, runtime, compiler, framework, execution provider, backend, and container image.
- Clock policy plus temperature, power, and throttling observations.
- Exact commands or configuration files for
trtexec/tensorrt_rtx, AMD runtime tools, and OpenVINObenchmark_app. - Raw per-iteration measurements, repeated-run variance, summary statistics, and plots of throughput and latency percentiles versus concurrency.
- Accuracy metrics, tolerances, pass/fail outcomes, unsupported operators, graph partitions, fallback paths, and precision changes.
This record turns a one-off number into an auditable experiment. It also makes a later driver, runtime, or model update comparable because the original assumptions remain visible.
How can I report the results clearly?
Use one row per GPU, backend, precision, batch size, and concurrency, not one row per product name. Include device-only and end-to-end columns, then add p50, p95, p99, throughput, power observations, and accuracy status. A short narrative should identify the best operating point for each latency budget and explain any fallback or tuning difference.
Keep illustrative vendor figures in a clearly labeled “documentation example” section. The TensorRT-RTX ResNet-50 result of 1,133.1 queries per second at 0.881485 ms mean latency and the Magpie example of 1,192.76 total tokens per second are not directly comparable, just as OpenVINO’s 1,024-input/128-output-token example is a workload definition rather than a score. Your own controlled runs, with shared artifacts and timing rules, are the evidence for a cross-vendor decision.
Frequently Asked Questions
Should I benchmark a vendor-optimized engine or a portable runtime first?
Start with a portable artifact and a documented baseline so the workload is comparable, then add optimized engines as separate configurations. This shows whether a gain comes from hardware, backend optimization, or both, and prevents an untuned reference from being compared silently with a production-tuned build.
How many warm-up iterations are enough?
There is no universal count because compilation, memory allocation, and clock ramp behavior differ. Continue until consecutive timing summaries stabilize, record the discarded count, and use a separate startup test if initialization time matters to your application.
Why can p99 latency rise while throughput barely changes?
The device may already be saturated, so extra requests wait in a queue instead of increasing completed work. Check concurrency, queue depth, host contention, and thermal or power traces; the p99 curve often reveals the service limit before average throughput does.
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No. Documentation examples use different hardware, engines, input contracts, and timing boundaries. Use them to understand each tool’s output, then make the purchasing or deployment decision from repeated runs that share your model, dataset, precision, and latency objective.
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