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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sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →For most teams, start with on-demand TPU capacity for a small validation run, then move repeatable, checkpointed work to Spot. Use Flex-start when a run needs a defined window of up to seven days, a calendar-mode reservation for a scheduled event lasting one to 90 days, and a one- or three-year committed-use reservation only after utilization is predictable. The cheapest chip-hour is not automatically the cheapest job: region, data movement, restart time, checkpoint storage, TPU topology, quota, and the risk of waiting for capacity all affect the bill.
How Google TPU rental pricing is structured
Google Cloud quotes TPU prices in US dollars per chip-hour. A chip-hour is one TPU chip running for one hour; a larger slice consumes more chip-hours per wall-clock hour. Your compute charge is therefore approximately:
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TPU cost = chip-hours used × rate per chip-hour
That calculation is only the starting point. Add persistent storage for checkpoints, data-transfer charges when data crosses regions, orchestration or queue time, and the cost of rerunning a preempted job. A region with a lower TPU rate can be more expensive overall if it is far from your data or users.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesGoogle states that Cloud TPU pricing varies by product, deployment model, and region. The figures below are examples from Google Cloud’s 2026 pricing table and should be checked against the live table before you purchase capacity.
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Compare the five ways to rent TPUs
| Consumption option | Availability and interruption | Price signal | Best fit | Commitment and limits |
|---|---|---|---|---|
| On-demand | Starts immediately when capacity is available; no preemption, but Google does not guarantee that enough physical capacity exists when you request it. | Baseline pay-as-you-go rate. | Urgent jobs, exploratory sizing, and workloads with uncertain end dates. | No term commitment. |
| Spot | Uses excess capacity. A Spot VM can be preempted at any time and may be stopped or deleted with 30 seconds’ notice. | Highly discounted; Google describes discounts of up to 70%. | Checkpointed pre-training, fine-tuning, batch inference, and interruption-tolerant simulations. | No term commitment; restart engineering is required. |
| Flex-start (Preview) | Requests a defined runtime from a dedicated pool, with a maximum run of seven days. | Published example for Trillium in the cited regions: $1.35 per chip-hour. | Experiments, fine-tuning, and dynamic inference provisioning that need a short, defined window. | Preview; documented for TPU7x, v6e, and v5p. |
| Calendar-mode future reservation | Reserves a specified start time and duration, improving certainty for the reserved window. | Up to 30% below on-demand, according to Google. | Scheduled training windows, launches, and seasonal spikes lasting one to 90 days. | Available for v5e, v5p, v6e, and TPU7x; committed-use discounts are not supported for this short reservation type. |
| One- to three-year future reservation with CUD | Capacity is planned for a long-term requirement. | Listed at 30%–55% below on-demand. | Predictable, continuously used training or inference. | Requires a committed-use discount and a manual request through Cloud Sales or an account manager; supports all TPU versions. |
On-demand TPUs: the baseline for uncertain work
On-demand is Google’s standard pay-as-you-go model. It offers maximum lifecycle flexibility and does not preempt a running workload. That makes it the safest starting point when you are still measuring model throughput, debugging a framework, or unsure when a project will finish.
“Immediate” does not mean “guaranteed.” Google describes on-demand availability as subject to physical resource constraints. A valid project, quota grant, and correct API configuration can still leave you waiting because the requested slice is not available in the selected region or zone.
When on-demand is the right choice
- You need to begin a high-priority job and cannot design around interruption.
- The run length and final TPU size are not yet known.
- You are performing a small benchmark to establish throughput before choosing a cheaper purchasing model.
- The workload has no durable checkpoint or cannot safely resume from one.
How to control on-demand cost
Use a small representative slice first, record steps per second and memory use, and stop the TPU as soon as the validation run ends. Estimate the full job from measured chip-hours rather than from wall-clock guesses. If repeated runs show stable utilization and restart safety, use those measurements to evaluate Spot or a reservation.
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Spot TPUs: the lowest unit cost when restart is acceptable
Spot TPUs consume excess Google Cloud capacity. Google describes Spot as “highly discounted,” with discounts of up to 70% in its TPU overview. The trade-off is reliability: Google says Spot VMs can be preempted at any time and may be stopped or deleted with 30 seconds’ notice.
Workloads that fit Spot
- Pre-training jobs that save frequent, restartable checkpoints.
- Fine-tuning runs where losing part of an epoch is acceptable.
- Batch inference that can retry failed shards.
- Monte Carlo, rendering, simulation, and other divisible work.
Build the restart path before switching
- Write model, optimizer, scheduler, random-state, and data-position state to durable storage. A weights-only checkpoint may not resume at the same training position.
- Checkpoint often enough that a 30-second interruption does not discard an unacceptable amount of work.
- Make the launcher idempotent so a retry does not corrupt output or duplicate an irreversible side effect.
- Detect preemption, release stale resources, and restart on a fresh TPU slice.
- Track total completion time and retry count, not just the discounted chip-hour rate.
A Spot run with a 60% lower rate can still cost more than on-demand if repeated interruptions add hours of TPU time, delay a deadline, or require expensive engineer intervention. For one-shot jobs with no validated recovery process, Spot is a false economy.
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Flex-start: a seven-day middle ground
Flex-start is documented as a preview option for TPU7x, v6e, and v5p. It requests a defined runtime of up to seven days from a dedicated capacity pool. Google positions it between on-demand flexibility and reservations: it is intended for experiments, fine-tuning, and dynamic inference provisioning when a short run needs better availability than an opportunistic Spot request.
Flex-start is useful when your work has a clear stopping point but does not justify a one-to-90-day reservation. Confirm that the preview is enabled for your project and selected TPU version before designing a production workflow around it. A seven-day maximum also means that a longer training job must be split into restartable segments or use another purchasing model.
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A calendar-mode future reservation specifies a start time and a duration from one to 90 days. It is designed for a known event: a training campaign, a model launch, a class or conference workload, or a seasonal inference spike. Google lists support for v5e, v5p, v6e, and TPU7x and says calendar-mode pricing can be up to 30% below on-demand.
This option trades some flexibility for start-time certainty. Select the reservation window only after you know when data will be ready and when the job can finish; paying for an idle reserved window removes the expected savings. Google states that committed-use discounts are not supported for this short reservation type.
Calendar reservation checklist
- Choose the TPU version, slice size, region, and start time before submitting the request.
- Align the window with data staging, checkpoint warm-up, and downstream validation, not merely the training command.
- Schedule a stop or release action so an early finish does not leave expensive resources running.
- Keep a fallback plan if the job overruns the reservation or requires a different topology.
One- and three-year reservations with a committed-use discount
Long-term future reservations cover one to three years and support all TPU versions. Google lists them at 30%–55% below on-demand and says they are ideal for long-running training and inference workloads. The discount is exchanged for a usage commitment: Google requires a committed-use discount, and the request is handled manually through Cloud Sales or an account manager.
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When the commitment pays
Compare the discounted rate with the cost of unused capacity. If you expect to use a slice for u hours per month, the annualized compute spend is roughly u × 12 × chips × discounted rate. Compare that with the commitment’s required spend, then add growth, maintenance windows, model migrations, and periods when a newer TPU generation may be preferable.
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A three-year rate can be the lowest nominal price, but it has the highest utilization risk. A one-year term is easier to align with a roadmap or model cycle. Do not commit solely because a percentage discount looks large; commit when measured demand is stable enough that unused hours are unlikely to erase the saving.
Region and TPU generation change the real bill
Google’s TPU generations differ in memory, interconnect, supported slice sizes, and other machine characteristics. Google’s TPU overview lists Trillium (v6e), v5p, and other generations; a lower chip-hour price is irrelevant if the model does not fit the available memory or topology.
Start by identifying the exact generation, topology, and slice size supported by your framework. Then check whether that configuration exists in the region where your data must remain. Moving a large dataset to a cheaper region can add transfer charges and latency, while serving users from a distant region can increase application response time.
Published Trillium (v6e) examples
The following figures are Google Cloud 2026 list prices in USD per chip-hour. They are regional examples, not a universal TPU rate.
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| Region | On-demand | Flex-start | Calendar mode | One-year commitment | Three-year commitment |
|---|---|---|---|---|---|
| us-east1 (South Carolina) | $2.70 | $1.35 | $1.89 | $1.89 | $1.22 |
| us-east5 (Ohio) | $2.70 | $1.35 | $1.89 | $1.89 | $1.22 |
| europe-west4 (Amsterdam) | $2.97 | $1.35 | $1.89 | $2.08 | $1.34 |
For scale, 100 Trillium chip-hours in us-east1 or us-east5 would be $270 on demand, $135 with the published Flex-start example, $189 in calendar mode or with the one-year rate, and $122 with the three-year rate. In Amsterdam, the same 100 chip-hours would be $297 on demand, $135 with Flex-start, $189 in calendar mode, $208 at the one-year rate, or $134 at the three-year rate. These are compute-only illustrations; storage, transfer, retries, and other services are extra.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Quota is not the same as capacity
TPU quota is an account and project limit; physical capacity is what Google can actually place in a region or zone. Quotas differ by TPU version and by on-demand versus preemptible use, and some versions require a quota request. A quota grant does not remove regional or zonal capacity constraints.
Check these prerequisites before scheduling a run
- The project has billing enabled and the TPU API and required permissions are configured.
- The requested TPU version and mode have the necessary quota.
- The chosen region and zone support the generation and topology you need.
- Your framework, runtime, and storage path support the selected TPU slice.
- For Spot, the job has durable checkpoints and an automatic retry path.
- For reservations, the requested window or long-term commitment has been approved.
If a request fails, first distinguish a quota error from a capacity error. Increasing quota will not solve a regional shortage; moving to another supported region or changing the slice may. Conversely, changing regions cannot fix a project that has not requested the correct version-specific quota.
A practical decision sequence
- Define the workload. Record model size, memory requirement, framework support, target throughput, expected duration, deadline, and whether the job can resume after interruption.
- Select the generation and topology. Confirm the TPU version, slice size, interconnect, and memory fit before comparing prices.
- Choose a data region. Account for residency rules, existing storage, transfer cost, and user latency.
- Secure quota. Request the correct quota for the TPU version and consumption mode; do this before the job date.
- Run a small on-demand validation. Measure throughput, memory headroom, checkpoint size, and restart time.
- Move repeatable batch work to Spot. Do this only after a restart has been tested and the cost of retries is understood.
- Use Flex-start for a defined short run. It is appropriate for up to seven days when the preview is available and a dedicated pool is useful.
- Reserve an event window. Use calendar mode for one to 90 days when a start time matters and demand is concentrated.
- Evaluate a CUD-backed term. Choose one or three years only when measured utilization is predictable and the discount exceeds the cost of unused commitment.
Calculate total job cost before choosing a model
Use the same workload measurement for every option. Multiply the number of chips by wall-clock hours and include expected retries:
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Total TPU chip-hours = chips × planned hours × (1 + expected retry fraction)
Then add:
- Checkpoint and dataset storage, including retained historical checkpoints.
- Data-transfer charges when storage, training, and serving are in different regions.
- Startup, queue, and provisioning time that keeps supporting services active.
- Engineering time for checkpoint recovery, retry orchestration, and operational monitoring.
- Deadline or revenue impact when preemption delays a launch.
Compare at least three scenarios: an on-demand baseline, a Spot case with a realistic retry fraction, and the reservation case with the hours you genuinely expect to consume. This exposes situations where a 70% unit discount is outweighed by interruptions or where a 30% reservation discount is lost to idle capacity.
Common failure modes and fixes
The TPU request is rejected despite approved quota
Quota only authorizes consumption; it does not create physical capacity. Verify the region, zone, version, and slice size. Try another supported location or a smaller configuration, and keep on-demand and Spot availability separate in your diagnosis.
A Spot job loses too much progress
Shorten the checkpoint interval, store complete training state durably, and make the launcher resume automatically. If the deadline cannot tolerate repeated restarts, price the same job on on-demand or a reservation instead.
The reserved window is underused
Revisit the start and end times, stage data before the window, parallelize independent jobs, and stop resources promptly when work finishes. Do not extend a commitment until utilization is measured over multiple runs.
The cheapest region produces a higher invoice
Include transfer, storage replication, latency, and operational overhead. Recalculate with the region that keeps the dataset and serving path close to the TPU, then compare the complete job cost rather than the chip rate.
The chosen TPU cannot run the model efficiently
Check memory, topology, supported slice sizes, and framework compatibility before signing a reservation. A nominally cheaper generation can require a larger slice or lower throughput, eliminating its price advantage.
Which option should you choose?
- Choose on-demand when the work is urgent, exploratory, non-checkpointed, or unpredictable in duration.
- Choose Spot when the workload is restartable and the lowest possible unit cost matters more than uninterrupted execution.
- Choose Flex-start when you need a short, defined run of up to seven days on TPU7x, v6e, or v5p and the preview is available.
- Choose calendar mode when a known event requires one to 90 days of planned capacity and a start-time guarantee is worth more than maximum flexibility.
- Choose a one-year CUD-backed reservation when demand is stable but your architecture or roadmap may change within a year.
- Choose a three-year CUD-backed reservation only when usage is consistently high, the TPU generation is likely to remain suitable, and unused commitment risk is acceptably low.
Recheck Google Cloud’s live TPU pricing and availability before purchase. The published numbers vary by generation, deployment model, and region, while quota and physical capacity can change independently.
Quick Recap
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