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What is Google TPU v4?
A Tensor Processing Unit (TPU) is a Google-developed application-specific integrated circuit designed to accelerate machine-learning workloads. TPU v4 is the fourth generation. When Google calls it a “supercomputer,” it is describing a networked system of chips and supporting infrastructure—not a standalone chip that consumers can buy.
Google’s 2021 announcement described a full TPU v4 Pod as 4,096 chips connected together, with a peak performance of 1.1 exaflop/s. That is a theoretical system peak, not a promise that every model or training run will sustain that rate. Actual throughput depends on the model, numerical format, parallelization strategy, software, communication demands, and how fully the system is used. Google’s TPU v4 announcement also discussed use in work including MUM and LaMDA, and named TensorFlow, PyTorch, and JAX among the frameworks supported at launch.
How TPU v4’s architecture supports large models
Optical switching and a 3D torus
TPU v4’s network is part of the machine’s design for scale. Google’s technical description says the system uses an internally developed optical circuit switch (OCS) to reconfigure connections and help route around failures. The chips use a 3D torus interconnect, rather than the 2D torus used in TPU v2 and v3. Google says the additional dimension improves bisection bandwidth—the capacity for data to move between sections of the system—which matters when distributed workloads exchange data frequently. Google’s TPU v4 engineering article describes the network and the company’s performance and efficiency comparisons.
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Google’s TPU v3 comparisons
Google reported that TPU v4 averaged 2.1× the per-chip performance of TPU v3 and 2.7× its performance per watt; it also said typical mean chip power was 200W. These are Google-published comparisons, not independent measurements. The same article claimed nearly a 10× increase in scaled system performance over TPU v3. The result for an individual workload may differ from these averages.
Google also compared TPU v4’s energy efficiency with contemporary machine-learning accelerators, claiming roughly 2–3× better efficiency and, under stated typical on-premise data-center assumptions, as much as roughly 20× lower CO2e. Those comparisons depend on Google’s methodology and facility assumptions; they should not be treated as universal measurements of every deployment.
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What large-model training results has Google reported?
MLPerf Training v1.1 runs
In its 2021 report on MLPerf Training v1.1, Google described two Open-division large-model benchmark runs:
- A 480-billion-parameter model trained on a 2,048-chip TPU v4 slice in about 55 hours.
- A 200-billion-parameter model trained on a 1,024-chip slice in about 40 hours.
Google calculated 63% computational efficiency for the runs using a measure that included model floating-point operations plus compiler rematerialization relative to system peak FLOPs. The company noted that computational efficiency and end-to-end training time were not official MLPerf metrics. These figures are Google’s reported results for specific benchmark configurations, not a general training-time estimate for other models. Google’s MLPerf v1.1 results provide its account of the entries.
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PaLM training
Google reported that its 540-billion-parameter PaLM model sustained 57.8% of peak hardware floating-point performance over 50 days of training on TPU v4 supercomputers. This is a workload-specific result reported by Google; it does not establish the same utilization for other models or customers. Google also said the interconnect supported multidimensional model partitioning for low-latency, high-throughput inference. The engineering article gives the PaLM figure and its system context.
MLPerf records and their limits
Google said TPU v4 achieved records in four of the six MLPerf benchmarks it entered in 2021, and that its best submission beat the fastest non-Google submission in the relevant comparisons. Benchmark rankings apply to particular workloads, system sizes, software, and submission rules; they do not show that TPU v4 is fastest for every model. Google’s MLPerf Training v1.1 post describes the broader results.
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Can you use TPU v4 through Google Cloud?
TPU v4 is offered as cloud infrastructure rather than as a retail accelerator. Google’s 2022 launch description covered Cloud TPU v4 Pod slices ranging from four chips (one TPU VM) to thousands of chips. It also reported 6 Tbps of bandwidth per host for that offering. These are historical launch details, not guarantees of current capacity. The same announcement described an Oklahoma Cloud TPU cluster with 9 exaflops of aggregate peak performance and 90% carbon-free energy, both Google-reported cluster-level figures. They are distinct from the 1.1-exaflop/s peak for one 4,096-chip Pod. Google’s 2022 cluster announcement provides that context.
Current region and pricing information
As checked on October 4, 2026, Google Cloud’s regions documentation lists TPU v4 configurations in zone us-central2-b and warns that higher-chip-count configurations are available only in limited quantities. The pricing page lists a TPU v4 Pod in region us-central2; it describes per-chip-hour pricing and notes that Cloud Console billing can display VM-hours. On that checked page, an on-demand v4 host—four chips plus a VM—was shown at $12.88 per hour. Cloud capacity, quotas, pricing, and availability can change, and neither listing guarantees that a particular project can obtain capacity. Check the live regions documentation and TPU pricing page for the intended region and configuration before planning a deployment.
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What software and setup considerations matter?
Framework support and runtime compatibility depend on the specific TPU generation and software versions. Google’s software-version guidance says TPU v4 and older use tpu-ubuntu2204-base for the listed PyTorch/JAX path and includes TPU v4-specific TensorFlow runtime guidance for older TensorFlow versions. Google also says the Cloud TPU API is no longer under active development and recommends Compute Engine or Google Kubernetes Engine (GKE) for newer TPU resource-management features. Consult the current TPU software version documentation for the framework, runtime, and TPU combination you intend to use rather than treating launch-era support as a current compatibility guarantee.
How to assess TPU v4 for a workload
Peak FLOPs alone are a poor basis for deciding whether a large model will train efficiently on TPU v4. Compare the system against the intended workload and deployment requirements:
- Training time or throughput: Look for results on a comparable model and workload, not just a peak-performance figure.
- Scaling efficiency: Determine how performance changes at the chip count you can actually obtain.
- Network and parallelism: Consider communication volume, interconnect topology, and how the model can be partitioned.
- Software fit: Check framework, compiler, runtime, and resource-management compatibility, including engineering effort to adapt the workload.
- Cost and capacity: Confirm current regional pricing, quota, and availability for the required configuration.
- Energy and carbon: Compare measurement methods and facility assumptions, not just headline efficiency claims.
Google’s published results show that TPU v4 was built to train and serve large models at system scale. They do not provide an independent head-to-head recommendation or establish that it is the best choice for every model, budget, or cloud region.
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