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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchDo not choose a custom AI chip from a peak teraFLOPS number or a discounted chip-hour. Before signing a reservation or long-term commitment, require the provider to prove that your exact model, software stack, topology and service targets can be delivered at a predictable total cost.
The questions below turn that principle into a procurement checklist. They cover workload evidence, software compatibility, capacity, pricing, operations, service-level agreements (SLAs), portability and exit terms. A chip that wins an isolated benchmark can still lose when compilation, data movement, idle capacity, engineering effort or an interrupted job is included.
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Start with a workload-specific decision, not a chip comparison
There is no universally best accelerator. The right choice depends on model size and precision, input and output shapes, batch size, concurrency, latency and throughput targets, training or serving pattern, topology, region, available capacity and the commercial commitment you are willing to make.
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AWS’s workload guidance explicitly treats model size and precision, latency and throughput service-level objectives (SLOs), workload type, cost and capacity as decision dimensions. Google Cloud likewise says TPU selection depends on computational characteristics, memory and scaling needs. Those are provider-specific examples, but the purchasing principle is general: define the work before comparing the hardware.
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Write the acceptance criteria first
- Quality: the agreed evaluation score, error rate or regression limit for the production model.
- Performance: tokens or samples per second, job completion time, and p50, p95 and p99 latency.
- Reliability: uptime, successful-job rate, recovery time and checkpoint-loss limit.
- Efficiency: sustained utilization and cost per useful token, sample or completed training run.
- Scope: the exact model revision, precision, context length, tensor shapes, batch sizes, concurrency and target topology.
Put these thresholds in the evaluation plan and, where possible, in the contract. A provider’s peak chip specification is not evidence that the service will meet them.
Questions about benchmark evidence
Can the provider reproduce your production workload?
- Which production model, checkpoint, precision, context or input shapes, batch sizes and concurrency did you benchmark?
- Was the test training, fine-tuning, batch inference, online serving or a mixture of these patterns?
- Which framework, compiler, runtime and library versions were used?
- What accelerator count, interconnect topology, host type and region were used?
- Can your team run the same code and data shape on the proposed configuration?
Require raw results rather than a single average. Ask for throughput, p50/p95/p99 latency, utilization, memory headroom, compilation time, warm-up time, retry count and failed-job rate. Insist that the provider disclose whether the result used synthetic data, cached inputs, unusually favorable batch sizes or a model variant that differs from yours.
What work is included in the measured cost?
A credible benchmark includes compilation, graph capture, warm-up, checkpointing, storage, network traffic, data transfer, retries and any host or orchestration time. Ask for both steady-state performance and the time and cost to start a job. For serving, measure scale-up and scale-down behavior, queueing and cold starts. For training, measure checkpoint frequency, restart time and the amount of work lost after a failure.
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Request results for a current-generation conventional GPU or another custom accelerator using the same model, precision, software version, topology, utilization assumptions and price basis. Label each result as a provider claim, your own measurement or a contractual guarantee. Without matched assumptions, a table of chip-hour rates is not an apples-to-apples comparison.
Questions about software, operators and migration
Verify the complete software path
Ask which framework and version, operators, quantization methods, dynamic-shape behavior, custom kernels and distributed-training features are supported for your workload. Identify every operation that falls back to a CPU or another accelerator, and quantify the performance and cost penalty of that fallback.
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AWS describes its Neuron SDK for Trainium and Inferentia as including a compiler, Neuron Kernel Interface, runtime, PyTorch and JAX integration, training and inference libraries, service integrations, and profiling and debugging tools. Those components are useful only if they cover your exact graph and release versions. Require an end-to-end demonstration, not a feature list.
AWS’s Trainium product page states, “PyTorch, vLLM, HuggingFace, Ray — they work without modification.” This is AWS marketing copy, not an independent test. Ask AWS to demonstrate the precise code path, operators and performance for your model; do not treat the sentence as proof that your workload will run unchanged.
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- List code changes, graph rewrites, compilation steps and custom kernels required before production.
- Estimate engineer-weeks for porting, profiling, debugging and performance tuning.
- Identify who owns a regression when a framework or runtime upgrade breaks compilation or throughput.
- Require a support lifecycle for the chip generation and advance notice of runtime or instance retirement.
Plan checkpoint and artifact portability
Ask how model weights, optimizer state, checkpoints, tokenizer assets, serving images and telemetry are exported. Test restoration on a second provider or a conventional GPU stack before committing. A portable checkpoint format does not guarantee portable performance; budget for revalidation, kernel replacement and numerical-difference testing.
Questions about capacity, topology and geography
Turn a capacity promise into a precise commitment
Have the provider state the accelerator model, chip count, host type, interconnect topology, region, zone and delivery date. Clarify whether the promise is a quota, a reservation, a dedicated pool allocation or merely an estimate. Ask what happens if the provider cannot deliver the requested capacity on the start date.
Google Cloud documents that on-demand TPU requests are subject to physical availability. Reservations can guarantee capacity only subject to approval and quota, while Spot capacity can be preempted. Its flex-start option targets a start within seven days from a dedicated pool. These terms apply to Google Cloud and should not be generalized to other providers.
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Ask how capacity behaves over time
- How quickly can the allocation scale up and down?
- Are minimum sizes, start and end dates, extensions and cancellation terms flexible?
- What capacity is exclusive to your account, and what is shared during regional scarcity?
- How are maintenance events handled when the reserved pool is unavailable?
- Which approval and quota steps remain, and who is responsible for completing them?
Check data-residency boundaries
Document the regions and zones used by accelerators, control-plane services, storage, telemetry, support access, training data and inference requests. Confirm whether backups, logs and remote support can cross borders. A reservation in a preferred region is not useful if the required storage or support path violates your residency policy.
Compare effective total cost, not a headline chip-hour
Define the billable unit
Ask whether charges are based on chip-hours, accelerator-hours, VM-hours, reserved capacity, committed spend or a combination. Google Cloud’s public TPU documentation lists prices by version, location and consumption model; the public page bills per chip-hour, while console usage may be displayed in VM-hours. Reconcile those units in writing before comparing quotes.
For AWS AI Factories, pricing is tailored to location, scale, selected accelerators and services, and existing infrastructure after a joint configuration assessment. Obtain customer-specific written pricing rather than inferring a rate from a public product page.
Build a cost-per-useful-output model
| Cost component | Question to ask |
|---|---|
| Accelerator and host | Are host CPUs, memory, local storage and management nodes included? |
| Fabric and networking | Are interconnect, cross-zone traffic, load balancing and data-transfer charges included? |
| Storage | What do checkpoints, datasets, snapshots and retained logs cost? |
| Utilization | What is the expected sustained utilization, and who pays for idle or warm capacity? |
| Failures and preemption | Are failed, interrupted or maintenance periods billed, and are credits automatic? |
| People and migration | What are the porting, profiling, revalidation and operational staffing costs? |
| Exit | What will data egress, parallel fallback capacity and contract termination cost? |
Calculate cost per completed training run, accepted sample, generated token or served request at expected and peak utilization. Include retries, checkpoint recovery and idle periods. A lower accelerator rate can be more expensive if it requires more chips, lower utilization or substantial engineering work.
Read the commercial fine print
Request minimum spend, term, take-or-pay, renewal, discount-clawback, price-change, cancellation and termination provisions. Ask whether discounts apply to all supporting services or only accelerator time. Define how credits are calculated, what evidence is required and whether credits can be used only on future service.
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Questions about reliability and day-to-day operations
Identify the actual SLA
Ask which service-specific SLA applies to the exact accelerator and instance configuration. Determine whether it covers instance availability, reserved-capacity delivery, networking, storage, a managed endpoint or only a control-plane API. Record the measurement interval, exclusions, claim deadline, evidence burden and remedy.
AWS says AWS AI Factories services have service-specific SLAs; obtain the commitments for the services in your design rather than assuming a generic cloud SLA covers accelerator capacity or completion of a training job.
Assign recovery responsibilities
Clarify who handles chip, host, fabric, compiler, runtime and managed-service incidents. Put support response times, escalation paths and severity definitions in the contract. Decide who patches hardware, announces maintenance, creates checkpoints, retries jobs and executes disaster recovery.
Google Cloud’s default TPU capacity is Google-managed. Its All Capacity mode for specified versions provides full use of reserved capacity and topology visibility, while assigning hardware maintenance and failure recovery to the customer. If you choose that mode, budget for the operational expertise and automation it requires.
Demand useful observability
- Job-level billing and allocation records.
- Accelerator utilization, memory, network and fabric counters.
- Compilation, kernel and queue-time diagnostics.
- Power and thermal information where available.
- Maintenance, preemption and failure-event logs.
Without these records, you cannot distinguish a model problem from compiler overhead, network contention or provider-side capacity loss.
Use specifications as context, not as a buying decision
Vendor-published figures help with capacity planning but are not a head-to-head benchmark. Google Cloud lists TPU7x (Ironwood) at 192 GiB of memory and 7,380 GB/s memory bandwidth per chip, with a 9,216-chip pod. TPU v6e (Trillium) is listed at 32 GiB and 1,638 GB/s per chip, with 256 chips per pod. TPU v5p is listed at 95 GiB, 2,765 GB/s and an 8,960-chip pod. AWS lists Trainium3 at 144 GB of HBM3e and 4.9 TB/s of memory bandwidth per chip. These are vendor-reported specifications, not guarantees for your configuration.
| Option | Published memory per chip | Published memory bandwidth | Published pod scale |
|---|---|---|---|
| Google TPU7x (Ironwood) | 192 GiB | 7,380 GB/s | 9,216 chips |
| Google TPU v6e (Trillium) | 32 GiB | 1,638 GB/s | 256 chips |
| Google TPU v5p | 95 GiB | 2,765 GB/s | 8,960 chips |
| AWS Trainium3 | 144 GB HBM3e | 4.9 TB/s | Not stated |
Google notes that TPUs may be a poor fit for workloads with frequent branching, CPU-dependent custom operators or FP64 requirements. Treat those as workload-screening guidance, then verify the behavior on your graph and precision.
Build a provider scorecard and pilot gate
Score six decision axes
- Matched performance and quality: Does the configuration meet the agreed SLOs and quality floor?
- Software coverage: Are operators, framework versions, dynamic shapes and distributed features supported?
- Capacity: Are region, topology, quota, delivery date and scaling rights guaranteed?
- Effective cost: What is the cost per useful output after all infrastructure and people costs?
- Operations: Who maintains, monitors and recovers the system, and under what SLA?
- Portability: Can you export artifacts and move workloads without prohibitive rework or egress?
Give each axis a pass/fail gate before assigning weighted scores. A provider that fails a mandatory residency, capacity or recovery requirement should not win through a lower price on another axis.
Make the pilot representative
Use production-like data shapes, concurrency, sequence lengths, checkpoint intervals and topology. Run long enough to expose thermal behavior, compilation caches, maintenance events and utilization drift. Have your team reproduce the provider’s result, then test a fallback platform and a failure-recovery drill. Preserve the scripts, environment manifests and raw telemetry as the baseline for contract negotiations.
Contract clauses to secure before signing
- The exact accelerator generation, count, topology, region and delivery date.
- Capacity remedies if quota, approval or physical availability delays delivery.
- Performance and quality acceptance tests tied to your model and software versions.
- Support severity targets, escalation contacts and ownership of compiler or runtime defects.
- Maintenance notice, checkpoint and restart responsibilities, and disaster-recovery obligations.
- Billing definitions for idle, failed, preempted, warm and maintenance periods.
- Price protection, renewal, cancellation, termination and discount-clawback terms.
- Data residency, telemetry access, export formats, egress pricing and exit assistance.
- Hardware or software refresh rights, migration support and notice of retirement.
Regulatory market context is broader than a buying decision: the UK Competition and Markets Authority’s final decision report, published July 31, 2025, identifies Google TPUs, AWS Trainium and Inferentia, and Microsoft Maia as examples of provider-developed accelerator ASICs and discusses their accompanying software. It does not establish current availability, performance or commercial terms for any particular buyer.
Red flags that should stop the commitment
- A benchmark uses a different model, precision, framework version or topology than your production workload.
- The provider will not disclose fallback operators, compilation failures or restart behavior.
- “Reserved” capacity is not distinguished from quota or an estimate.
- A price excludes hosts, networking, storage, data movement or idle time.
- The SLA covers a managed endpoint but not the accelerator capacity your jobs require.
- No party is named for checkpointing, hardware failure, compiler regressions or disaster recovery.
- Export, egress, termination and refresh terms are absent or left to a future order form.
Commit only when the provider has demonstrated the exact workload, the capacity and recovery model are written down, and the total-cost and exit assumptions survive an independent review by engineering, finance, procurement and security.
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