The Tool Desk
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How do I benchmark TPU performance before moving my AI workload?
Use a controlled test that represents the work, operating conditions, and deployment path you expect in production. A benchmark is useful only if it answers a decision: whether a particular TPU configuration can meet your quality, performance, memory, cost, and operational requirements. The procedure below is a practical benchmark plan, not a universal benchmark standard prescribed by Google.
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1. Define the workload and success criteria
Write down the workload before running it. Record the model and code version, framework, input data and shapes, precision, training batch size or inference request distribution, target quality, sequence lengths where applicable, and number of devices and hosts. Include any workload characteristics that affect the result, such as variable input lengths or preprocessing performed outside the accelerator.
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Set the pass criteria before comparing systems. A training run might need to reach a fixed quality target within a time or cost limit. For inference, define a throughput target and a latency limit at a stated percentile under the expected request mix. Decide what counts as useful completed work: examples, tokens, requests, or a finished training job. Without that definition, a throughput number can be impressive but irrelevant.
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Keep these workload conditions fixed between TPU and GPU runs as far as the hardware and software permit. If a configuration requires a different batch, precision, or parallelism strategy, report that difference rather than presenting the runs as identical. The result then describes two complete execution setups, not an isolated hardware comparison.
2. Choose the TPU deployment you could actually use
Select a TPU generation and topology according to the model’s compute, memory, and scaling needs, then test the production-relevant configuration. Google’s Cloud TPU overview lists TPU7x (Ironwood), TPU v6e (Trillium), and TPU v5p with different specifications; those numbers help shortlist candidates, but they do not predict application speed by themselves. Where a multi-chip or multislice deployment is under consideration, test that scale too: communication can materially affect the result.
Record the hardware version, topology, number of devices and hosts, framework, compiler and runtime versions, and provisioning path for every run. A result from one generation, framework release, or deployment arrangement should not be assumed to describe another.
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TPU workloads use XLA compilation. The first execution can include compilation and startup work, so distinguish those costs from sustained execution. If startup time affects a short-lived job or a serving system that frequently starts workers, measure and report it; do not quietly fold it into a steady-state throughput figure. For a long-running job, report steady-state performance after compilation and warm-up have completed.
State how many steps or requests were used for warm-up and which interval was measured. Use comparable measurement windows across configurations, and repeat the runs. A short sample can be distorted by initialization, transient input stalls, or other temporary variation. Preserve the raw timing data and explain how the reported value was calculated rather than reporting only a single best run.
4. Measure completed work, not just accelerator activity
For training, report examples or tokens per second, step time, and elapsed time to the fixed quality target. Throughput alone can mislead if a system processes examples faster but requires more steps, changes the achievable quality, or takes longer overall to converge. For inference, report throughput alongside latency percentiles for the same request distribution and quality target. A high aggregate request rate does not establish that interactive requests meet their latency objective.
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Record memory behavior at the batch or request shapes that matter. Also calculate the cost of completing the same useful work, including relevant host and data-pipeline resources rather than accelerator time alone. If an experiment does not finish because of a memory limit or cannot sustain the target load, record that outcome; it is meaningful evidence for the migration decision.
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Google’s Cloud TPU performance guide says the first step in troubleshooting TPU performance is to profile the model. Use a profile to explain bottlenecks rather than trying to infer them from a single utilization percentage. XProf supports programmatic capture for selected steps or code blocks, as well as on-demand capture when a profile server is enabled. Profiles can be stored on the TPU VM or in Cloud Storage and inspected with TensorBoard.
Capture comparable portions of each run. If you profile only one configuration or a different phase of execution, the traces may not explain the performance gap. Keep the profile alongside the benchmark record so that a later change in throughput can be connected to a change in execution behavior.
6. Change one major factor at a time
Use the profile and workload record to form a specific diagnosis. If you change batch size, input handling, sharding, precision, and topology together, a faster result will not reveal which change mattered. Adjust one major factor, repeat the measurement, and retain the prior run as a baseline. Verify that any performance improvement still meets the same quality and serving requirements.
How can I compare TPU and GPU performance fairly?
There is no universal head-to-head TPU-versus-GPU result for an unspecified workload. The comparison must be measured for the model, software stack, operating conditions, and target hardware you plan to use. Google’s published accelerator figures are specifications, not measured outcomes for your application.
Compare complete, viable configurations. Keep model behavior and quality target constant, use the same data and request mix, and state any unavoidable differences in framework, precision, batch size, or parallelism. Include the input pipeline and relevant host resources in the end-to-end result. If one setup requires extra engineering or a different serving arrangement, include that in the decision rather than treating it as free.
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| Comparison dimension | What to report | Why it matters |
|---|---|---|
| Useful work completed | Training examples or tokens per second and time to a fixed quality target; or inference requests per second at a stated latency target. | Shows whether the system completes the actual task at the required quality and pace. |
| Serving latency | Latency distribution, including the percentiles relevant to the service, under the same representative request mix. | Aggregate throughput can conceal slow requests or a poor user experience. |
| Cost for the work | Total relevant compute, host, and data-pipeline cost for the same useful output. | Faster execution is not automatically less expensive if the configuration uses more resources or requires more elapsed time. |
| Memory fit | Memory use and whether the target batch or request shapes fit without changing the intended workload. | A configuration that cannot fit the required model or batch is not a practical candidate. |
| Scale and communication | Throughput and efficiency at the intended device and host count, plus profile evidence about communication behavior. | Multi-chip and multislice performance may be limited by communication rather than individual-chip compute. |
| Operational fit | Framework support, deployment control, orchestration needs, and migration work required. | A benchmark win may not justify a deployment that cannot meet the team’s operational constraints. |
Compare repeated runs and state the measurement window, metric definitions, and any variation observed. Do not select only the best run from one system and compare it with a typical run from another. If the difference is small or unstable, treat it as inconclusive until you can reproduce it under the intended conditions.
What should I measure besides peak FLOPS?
Peak FLOPS describe a hardware capability under specified numeric formats; they are not a prediction of model throughput. Google’s Cloud TPU overview lists these peak per-chip figures in its 2026 documentation:
| TPU generation | BF16 peak compute per chip | FP8 peak compute per chip |
|---|---|---|
| TPU7x (Ironwood) | 2,307 TFLOPs | 4,614 TFLOPs |
| TPU v6e (Trillium) | 918 TFLOPs | 918 TFLOPs |
| TPU v5p | 459 TFLOPs | 459 TFLOPs |
These are published per-chip specifications, not results from a benchmark of your model. They do not show how much time your run spends on matrix operations, moving data, executing non-matrix operations, waiting for input, or communicating across devices. They also do not establish cost, memory fit, latency, or quality for your specific deployment.
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For a migration decision, pair completed-work metrics with memory behavior, latency where relevant, and cost for the same work. In a training comparison, time to a fixed quality target can be more informative than peak step rate. In inference, throughput without a latency distribution can hide a configuration that fails the service objective.
How do I profile a model running on Cloud TPU?
XProf is the profiling workflow described in Google’s Cloud TPU documentation. You can capture selected steps or code blocks programmatically, or capture on demand when the profile server is enabled. Store the resulting profile on the TPU VM or in Cloud Storage, then inspect it in TensorBoard. Follow the instructions for the framework and environment you are running; capture options and setup can vary by stack.
Google Cloud’s JAX/TPU documentation states that XProf uses TPU-integrated profiling features to enable collection overhead of less than 1%. This is a vendor-reported figure from that documentation, not a universal guarantee for every framework, run, or configuration. For a strict comparison, capture similar windows and account for the collection procedure in both runs.
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Use the view that answers the diagnostic question
- Trace Viewer: inspect the execution timeline for gaps, synchronization, host/device activity, and communication patterns.
- HLO Op Profile: see time by operation category and determine whether the run spends its time in the operations you expect.
- Memory Viewer: examine allocations and memory behavior when a target batch or shape is near the available limit.
- Roofline Analysis: assess whether operations are more constrained by compute or memory movement.
- Graph Viewer: inspect the compiled HLO graph when execution structure, fusion, or shape behavior needs investigation.
These views are complementary. A roofline view can suggest a compute-versus-memory constraint, for example, while a trace can reveal that the overall run spends substantial time waiting between device work. Use the profile together with the benchmark’s end-to-end timing, not as a replacement for it.
How do I diagnose a slow or disappointing TPU result?
Start with the profile and work through the likely constraints. Google’s Cloud TPU performance guidance highlights input-bound execution, batch size and sharding, memory management, network performance, XLA padding, and tensor dimensions. These factors interact, so test a diagnosis with a controlled change rather than assuming the first plausible explanation is correct.
Input delivery or host-side gaps
If the timeline shows device work separated by idle gaps, inspect whether input delivery or host-side work is keeping pace. Check the data path used in the benchmark and make sure preprocessing and data loading represent the intended production setup. A benchmark that feeds preloaded data may not describe a pipeline that must decode, transform, or retrieve inputs as part of each request.
Batch size, sharding, and transfers
Review whether the batch is large enough for the intended execution and whether data is sharded as expected across devices. Check host/device transfers and the placement of work between the host and accelerator. A changed sharding plan can affect both throughput and communication, so record it and compare only against a baseline with the same plan or clearly document the difference.
Memory pressure and shape padding
Memory use can limit the batch or request shapes that fit. XLA may pad shapes to meet execution requirements; padding can waste compute capacity and increase memory use. Inspect the actual tensor dimensions and shape behavior rather than assuming that nominal input size equals the work presented to the device. Variable shapes can also trigger compilation behavior that makes a run different from a fixed-shape steady-state test.
Network and multi-device communication
For multi-chip or multislice workloads, examine the profile for communication and synchronization costs. More devices do not guarantee proportionally more useful work: the model’s communication needs and the selected topology matter. Benchmark at the intended scale and report the device count so readers of the result can distinguish single-device behavior from scaled execution.
Non-matrix operations or dynamic shapes
TPUs are specialized for matrix operations. Google’s TPU introduction cautions that programs dominated by non-matrix operations such as add, reshape, or concatenate may not achieve high MXU utilization. It also notes that dynamic tensor shapes are not well suited because XLA compiles the graph for the first batch. This does not mean every model with those operations is unsuitable; it means the operation mix and shape behavior should be checked in the actual profile.
Is my model a good fit for a TPU?
A model is a stronger candidate when its important work maps well to the TPU’s matrix-oriented execution, its shapes and memory demands fit the intended configuration, and it can meet the end-to-end target at the required scale. This is a workload judgment, not a label that can be assigned from model family or parameter count alone.
Dynamic shapes and workloads dominated by non-matrix operations can be less suitable. Padding may leave hardware capacity unused and increase memory requirements; the details of shape tiling and padding vary by TPU generation. The decisive evidence is a representative run and profile: check whether the compiled work reflects your expected shapes, whether memory fits, and whether the time is spent on operations that can benefit from the TPU.
- Promising signs: the workload has substantial matrix computation, stable or manageable shapes, acceptable memory behavior, and useful throughput at the target device count.
- Reasons to investigate further: frequent shape variation, large padding overhead, a profile dominated by non-matrix operations, input stalls, memory pressure, or communication costs that rise with scale.
- Decision rule: proceed only when the representative workload meets quality, latency or time-to-result, memory, cost, and operational requirements—not merely because the accelerator has a high published peak figure.
What should the benchmark report include?
A result others can reproduce needs more than a headline throughput number. Keep the run record with enough detail to distinguish the workload, environment, measurement method, and profile. Use the same record format for every candidate configuration.
- Model, code, data, and input-shape versions or identifiers.
- Framework, compiler, runtime, and relevant software versions.
- Accelerator generation, topology, device and host counts, and provisioning path.
- Precision, batch size or inference request distribution, sequence lengths, and target quality.
- Compilation and startup treatment; warm-up definition; measurement window; number of repetitions.
- Exact metric definitions, including throughput units, latency percentiles, memory measure, and cost scope.
- Profile capture window and storage location, plus the profile findings used to explain the outcome.
- Any changes from the comparison baseline and whether quality and workload behavior remained equivalent.
Preserve both measured values and the context needed to interpret them. If a later software or hardware change alters the result, the record should make it possible to determine whether the workload, measurement, or configuration also changed.
How does TPU provisioning affect a migration decision?
The execution path is part of the deployment decision. Google’s Compute Engine documentation says the legacy Cloud TPU API is no longer under active development and will receive bug fixes and security updates only. It identifies Compute Engine and Google Kubernetes Engine (GKE) as migration paths, and says TPU7x and later new hardware generations are supported through Compute Engine or GKE.
Google recommends Compute Engine when direct VM-level control or custom operating-system images are needed. It recommends GKE for containerized workloads, automated scaling, and large-scale orchestration. Include the intended path in the benchmark because deployment control and orchestration affect the practical effort and behavior of a production system.
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Existing resources created through the legacy API are incompatible with Compute Engine and GKE. Moving them therefore involves recreating resources and rewriting scripts that depend on the legacy API; it is not simply a matter of changing a benchmark’s hardware selection. Include that migration work in project planning and validate the new provisioning path early enough that it does not become a late blocker.
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