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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThe only fair way to compare GPUs, TPUs, and other custom accelerators is to benchmark the complete system on the workload you actually intend to run. Define the model, data shapes, precision, quality target, load pattern, software stack, device count, and measurement boundary before recording a number. Then report latency or time-to-quality, throughput, scaling, utilization, and cost under those stated conditions.
A peak figure detached from its model, quality target, host, software, and scale is not a portable ranking. The procedure below turns an accelerator comparison into a reproducible engineering decision rather than a contest between selected vendor results.
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Start with the decision your benchmark must answer
Write the decision in one sentence before choosing hardware or code. “Which system serves this model at p99 latency below 200 milliseconds?” requires a different test from “Which platform reaches the target validation score fastest?” or “What is the cost per million generated tokens?” A benchmark that answers a different question can be technically accurate and still useless for your deployment.
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Typical decisions and the matching evidence
| Decision | Evidence to collect |
|---|---|
| Select an inference platform | Quality at the required level, throughput at stated concurrency or arrival rate, p50/p95/p99 latency, and capacity limits |
| Select a training platform | Elapsed time to a defined quality target, sustained throughput, utilization, convergence behavior, and scaling efficiency |
| Estimate operating cost | Cost for the same completed workload and quality, including host, networking, storage, and unused capacity assumptions |
| Plan a multi-accelerator cluster | Single-device baseline, absolute throughput by device count, communication overhead, and efficiency of scaling |
Choose a representative model checkpoint and an input distribution that resembles production. Include the largest and most common input or output shapes if they stress memory differently. Fix the quality target before testing precision or implementation changes; otherwise an apparently faster result may simply be doing less work or producing lower-quality output.
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Define the workload precisely
Record the workload as a specification that another engineer could implement without guessing. The specification should identify:
- Training, offline batch inference, online inference, fine-tuning, retrieval-augmented generation, or another task.
- Model name, exact version or commit, checkpoint, tokenizer, and relevant configuration.
- Input and output dimensions, sequence lengths, image or audio sizes, and any dynamic-shape distribution.
- Batch size, request concurrency, arrival-rate pattern, and whether requests are static or variable length.
- Numerical format, such as FP32, BF16, FP16, or an integer mode, plus accumulation and fallback rules.
- Dataset, preprocessing, augmentation, sampling, and train/validation split.
- Target quality: for example, an accuracy, loss, perplexity, BLEU-like score, or a task-specific acceptance test.
MLPerf Inference makes this principle explicit by tying tests to defined datasets, scenarios, and quality targets. Use that model even for an internal benchmark: a throughput number has meaning only when the computation and required output quality are fixed.
Set a measurement boundary before timing
Decide which parts of the path represent the application. For an online service, the boundary may begin when a request reaches the host and end when the response is ready. For an offline kernel study, it may begin after data is already resident on the device. Neither boundary is universally correct; hiding transfer or preprocessing costs simply makes the result inapplicable to a different deployment.
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Components to include or exclude explicitly
- Tokenization, decoding, image transforms, feature extraction, and other preprocessing.
- Host-to-device and device-to-host transfers.
- Data-loader work, input staging, and storage reads.
- Kernel execution, synchronization, collective communication, and checkpointing.
- Postprocessing, detokenization, ranking, and response serialization.
- Network hops between clients, hosts, and accelerators.
- Compilation, graph capture, autotuning, and one-time memory allocation.
Report the boundary in the headline of every result table. If compilation is excluded, show its duration separately and state whether it is paid once, repeated after shape changes, or incurred for every job. Google Cloud’s cross-accelerator methodology highlights transfer rates as a relevant benchmark dimension; a device with impressive kernel speed can lose at the application boundary if data movement dominates.
Describe the complete system, not just the chip
An accelerator result belongs to a configuration. Record the accelerator model and count, host CPU and memory, storage, interconnect, topology, and network fabric. Note whether devices share a host, span hosts, or use a managed service. Memory capacity and bandwidth can determine whether a model fits without sharding, while interconnect bandwidth and latency determine how much multi-device work is lost to synchronization.
Minimum system record
- Accelerator model, memory size, device count, and partitioning or virtual-device mode.
- Host processor, system RAM, NUMA layout, and storage type where input or checkpoint I/O matters.
- Device interconnect, host PCIe or equivalent link, cross-host network, and topology.
- Driver, firmware, runtime, compiler, framework, kernel libraries, collective-communication library, and benchmark version.
- Power or energy instrumentation and the exact boundary used for readings.
- Cloud region, instance type, reservation or on-demand term, and pricing timestamp when cost is reported.
The system is what delivers the workload result. Swapping a host, link, runtime, or library can change performance without changing the accelerator silicon, so do not present a system measurement as a universal property of a chip.
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Choose metrics that match inference
Inference needs both speed and service behavior. Report latency percentiles and throughput at named operating points. At minimum, provide p50 and p95; add p99 when tail behavior affects a service-level objective. State whether latency is measured per request, per sequence, per image, or per generated token.
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Load conditions are part of the result
Run at the concurrency or arrival rates the service must support. A maximum-throughput run with an unbounded queue can conceal unacceptable tail latency. Conversely, a single-request latency test says little about a batched service. Sweep load until the target latency is reached, saturation appears, or memory limits prevent additional concurrency.
For generative models, separate prompt processing from token generation when the application treats them differently. State input and output lengths, batching policy, and whether reported throughput counts input tokens, output tokens, requests, or complete sequences. Keep the quality target identical across implementations; quantization, speculative decoding, or accelerator-specific kernels must be validated against that target.
Choose metrics that match training
Training comparisons should report time to a defined quality or convergence target, not only steps per second. Include sustained examples-per-second or tokens-per-second throughput, accelerator and system utilization, and the number of devices. A faster step rate can still produce a slower result if it requires more steps to reach the same quality or if communication causes unstable scaling.
Convergence and stochastic variation
Training is stochastic. Seed, data order, optimizer settings, checkpoint, and stopping rule can change the path to the target. Repeat complete runs when practical, or report the variation across representative runs and explain any fixed-seed limitation. Compare completed runs at the same quality threshold rather than selecting the fastest partial trace.
The MLPerf Training work identifies time-to-solution, stochastic variation, and hardware/software diversity as central benchmark challenges. Treat throughput as a diagnostic metric and time-to-quality as the primary decision metric when the goal is a trained model.
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Make software differences visible
Different accelerators often require different kernels, graph compilers, memory layouts, and communication libraries. A fair comparison does not require identical binaries; it requires equivalent work and transparent implementations. Record optimization flags, operator fusions, compilation mode, graph-capture settings, sharding, recomputation, quantization, and any accelerator-specific code.
Publish the benchmark harness, configuration files, model revision, and environment manifest when possible. If one platform uses a vendor-tuned library and another uses a generic implementation, say so and distinguish “best available production stack” from “same portable code.” Validate that optimizations preserve the agreed quality target and numerical behavior.
Use a controlled test procedure
- Freeze the workload. Save the model, checkpoint, dataset revision, input-shape distribution, precision, quality test, and stopping rule.
- Prepare each system. Install the recorded driver, runtime, compiler, framework, libraries, and benchmark version. Verify device count, memory visibility, and topology.
- Warm up and compile. Run enough iterations to complete lazy compilation, graph capture, memory allocation, and cache population. Report these phases separately unless they recur in production.
- Measure steady state. Use a fixed duration or iteration count, synchronized timers, and the declared boundary. Capture throughput, latency distribution, utilization, memory use, and power where available.
- Repeat. Run multiple repetitions, preserve raw results, and report median and spread or confidence intervals rather than the best run alone.
- Check quality. Evaluate the same validation or acceptance set after each inference or training configuration. Reject a faster configuration that misses the target.
- Test the operating range. Sweep batch size, concurrency, sequence length, or device count as relevant. Stop when the application target is met or the system saturates.
- Archive the environment. Store logs, code revision, configuration, hardware inventory, pricing assumptions, and timestamps so the result can be reproduced after software changes.
Benchmark scaling instead of extrapolating it
Measure one, two, four, and larger device counts when the deployment will scale. Report absolute throughput and scaling efficiency relative to the single-device baseline:
scaling efficiency = measured throughput at N devices ÷ (N × single-device throughput) × 100%
Explain whether the workload is data-parallel, tensor-parallel, pipeline-parallel, or a hybrid. Capture communication time, synchronization stalls, host input limits, and memory pressure. A single-device result cannot predict a cluster result because collective operations and network contention grow with scale.
Test the topology you will deploy. A tightly connected group of devices and the same number spread across hosts are different systems. Include startup, checkpoint, and failure-recovery costs when they materially affect the training schedule or service capacity.
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Measure bottlenecks and utilization
Use utilization counters as clues, not as a performance score. Low accelerator utilization may indicate input starvation, synchronization, memory bandwidth limits, host preprocessing, or small batches. High utilization does not prove useful work if the implementation performs extra operations or misses the quality target.
Questions to answer for an outlier result
- Is the model fitting in device memory, or is paging and rematerialization occurring?
- Are host-to-device transfers overlapping computation, or serializing each batch?
- Is the host CPU or data loader limiting the accelerator?
- Does the interconnect carry gradients, activations, or replicated parameters at the measured rate?
- Are kernels compiled for the exact shapes and precision, or falling back to slower paths?
- Does a higher batch or concurrency level improve throughput while violating latency or quality requirements?
Compare cost and energy on equal work
Cost per hour is not a workload comparison. Calculate cost for the same completed inference volume or the same training run to the same quality target. State the price source, region, billing term, accelerator and host resources, networking, storage, licensing, and whether idle or reserved capacity is included.
For training, a useful measure is cost to target quality, not cost per step. For inference, report cost per request or per input/output token at the selected latency-throughput operating point. If reporting energy, define whether readings cover the accelerator only, the host, the entire server, or facility overhead. Do not combine a vendor’s performance-per-dollar claim with a different provider’s price and call the result independent.
Interpret published benchmark claims carefully
Published numbers are evidence for the exact test described, not universal rankings. Google Cloud’s 2024 discussion of Trillium MLPerf 4.1 training results reports up to 1.8× better performance per dollar versus the prior-generation Cloud TPU v5p. That figure is specific to the cited benchmark, comparison generation, pricing assumptions, and workload; it does not establish a general advantage over every GPU or accelerator.
The same discussion reports 99% throughput scaling efficiency for its specified evaluation. Treat that as a vendor-published result for the stated system and scale, not as an inherent property of the accelerator family. When reading any published table, check benchmark version, model, quality target, device count, software stack, and measurement boundary before comparing cells.
Build a result table that preserves context
Use one row per genuinely comparable configuration. Keep unlike tests in separate tables instead of averaging them into a misleading score.
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- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
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| Field | What to record |
|---|---|
| Workload | Task, model/checkpoint, dataset, shape distribution, and precision |
| Quality | Metric, target, evaluation set, and pass/fail result |
| System | Accelerator model and count, host, memory, interconnect, and topology |
| Software | Driver, runtime, compiler, framework, libraries, benchmark version, and optimization notes |
| Measurement boundary | Included preprocessing, transfers, synchronization, postprocessing, compilation, and network work |
| Load | Batch, concurrency or arrival rate, sequence lengths, warm-up, duration, and repetitions |
| Result | Latency percentiles or time to quality, throughput, utilization, memory, and scaling efficiency |
| Economics | Price source, region, term, resource inclusions, energy boundary, and timestamp |
If a value is not established for a configuration, write “not stated” and identify the source. Never fill a missing cell with a value inferred from another benchmark.
Common benchmark failures and fixes
Using peak throughput as the verdict
Symptom: A specification-sheet or unconstrained run looks excellent, but the service misses its latency target. Fix: Report throughput at the required concurrency and include tail latency.
Timing only accelerator kernels
Symptom: The result improves while end-to-end response time does not. Fix: Add preprocessing, transfers, synchronization, and postprocessing to an application-boundary measurement, while retaining a separate kernel breakdown.
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Symptom: Lower precision or aggressive optimization wins on speed but changes outputs. Fix: Freeze the quality target and run the same acceptance test before accepting the configuration.
Assuming linear multi-device scaling
Symptom: Adding devices produces little additional throughput. Fix: Measure communication, topology, synchronization, and host limits at every device count.
Reporting the best run
Symptom: Results cannot be reproduced or vary widely. Fix: Separate warm-up from steady state, repeat runs, preserve raw logs, and publish spread with the environment.
Mixing benchmark versions or systems
Symptom: A table combines different model revisions, software releases, device counts, or price assumptions. Fix: Split the tables and label every boundary; do not rank unlike rows.
A practical decision rule
Choose the accelerator configuration that satisfies the required quality and service or training target at the lowest acceptable total cost and operational risk. If two systems meet the target, compare reproducibility, software maintenance, memory headroom, scaling behavior, and geographic availability rather than declaring a winner from a single peak number. Keep the raw benchmark package and rerun it whenever the model, compiler, driver, pricing, or deployment topology changes.
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