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Do not choose a custom AI chip from a headline specification or hourly rate. Before signing a reservation or long-term commitment, require workload-specific benchmark evidence and written terms for software compatibility, capacity, total cost, operations, service levels and exit. A chip that looks efficient in isolation can be uneconomical or risky when your model, topology, utilization and recovery obligations are included.
Start with a decision brief, not a chip comparison
Write down the production service or training program you are buying capacity for. The brief should identify the model version, parameter size, precision, context and input shapes, batch size, concurrency, training or inference pattern, target regions and expected growth. Define measurable service objectives before a provider runs a demonstration.
- Quality floor: the accuracy, loss, safety or evaluation threshold that cannot be sacrificed for speed.
- Performance: tokens or samples per second, job completion time, and p50, p95 and p99 latency.
- Availability and utilization: uptime objective, minimum useful utilization and acceptable interruption rate.
- Economics: maximum cost per completed training run or per useful inference output, not merely cost per accelerator-hour.
- Capacity: chip count, topology, region, start date and scaling range.
AWS workload guidance identifies model size and precision, latency and throughput SLOs, workload type, cost, capacity, region and reservation strategy as decision dimensions. Those dimensions are a useful starting checklist, but your acceptance criteria must be specific to your application.
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Ask the provider to disclose exactly what was measured. A benchmark that changes model revision, sequence length, framework release or parallelism strategy is not an apples-to-apples result.
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Questions about the test setup
- Which production model, tokenizer, precision, quantization method, context length, input shapes, batch sizes and concurrency were used?
- Was the workload training, fine-tuning, batch inference, streaming inference or an interactive service?
- What accelerator count, host type, interconnect topology, storage and network path were used?
- Which framework, compiler, runtime, kernels and distributed-training libraries were installed, and which versions?
- Are the results reproducible with your code, data shape and target topology? Can your team run the same test in the provider account?
Insist on end-to-end accounting
Have the provider include compilation, graph capture, warm-up, data loading, checkpointing, retries, storage, network traffic and data transfer. Request both steady-state and first-request measurements. For an inference service, measure the complete request path, including queueing and host-side preprocessing; for training, include checkpoint writes and restart time.
Require a result sheet that reports quality alongside speed. A faster run that fails your evaluation threshold is not a successful optimization. Record who measured each number, the test date, software versions, topology, utilization and whether the figure is a provider claim, your own measurement or a contractual guarantee.
Verify software coverage and migration effort
Custom silicon is a software decision as much as a hardware decision. Ask for an operator-by-operator compatibility review rather than a general statement that a framework is supported.
Map the complete execution path
- Which framework and release support the model, and which operators, dynamic shapes, custom kernels and quantization paths are available?
- What must change in model code, graph capture, compilation flags, distributed strategy or serving configuration?
- Which operations fall back to a CPU or another accelerator, and what latency or throughput penalty does that create?
- How are unsupported operators detected in continuous integration before a production deployment?
- Who debugs a compiler or runtime regression after a framework upgrade, and what response time is committed?
AWS describes its Neuron SDK for Inferentia and Trainium as including a compiler, Neuron Kernel Interface, runtime, PyTorch and JAX integration, training and inference libraries, service integrations, and profiling and debugging tools. AWS’s Trainium product page states, “PyTorch, vLLM, HuggingFace, Ray — they work without modification.” That is a vendor marketing statement, not independent evidence. Ask AWS to demonstrate the exact model, versions and serving path you intend to operate, and measure performance yourself.
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Plan portability before migration
Document how checkpoints, weights, optimizer state, tokenizer assets, containers and serving images are exported. Test a restore on a conventional GPU stack or a second provider before committing. Identify proprietary kernels, compiler artifacts and monitoring integrations that would have to be replaced. Obtain the software-support lifecycle for the chip generation, including retirement notice periods and the policy for runtime and driver compatibility.
Make capacity a written deliverable
Capacity is not the same as a quota or an optimistic estimate. Your order should specify accelerator model, quantity, topology, region or zone, reservation start date and scaling behavior.
Clarify the capacity mechanism
- Is the commitment a quota, an approved reservation, a dedicated pool or shared capacity?
- What approval, quota and networking steps remain before the start date?
- What remedy applies if the provider cannot deliver the promised capacity on that date?
- Can you add or release capacity, and how quickly? Are minimum sizes, extensions and cancellation fees fixed?
- What happens during regional scarcity, host failure or maintenance?
Google Cloud documentation says on-demand TPU requests are subject to physical availability. Its reservations provide future capacity subject to approval and quota; Flex-start capacity can run for up to seven days from a dedicated pool; and Spot capacity can be preempted. These are Google Cloud-specific terms, so obtain the equivalent definitions for any other provider instead of assuming the same behavior.
Check geography and data boundaries
List the permitted regions and the consequences of a regional shortage. Confirm where control-plane metadata, storage, telemetry, support access, training data and inference traffic are processed. Ask whether a failover region has the same chip generation, topology and software image, or whether recovery would require a new validation cycle.
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Compare effective cost for completed work
Build a cost model around a completed training run, accepted model artifact or useful inference output. Include the host, accelerator, fabric, storage, snapshots, network traffic, data movement, orchestration, support, idle or warm capacity, engineering and migration.
Ask how billing is measured
- What is the billable unit: chip-hour, VM-hour, accelerator-hour, reserved capacity or committed spend?
- Are hosts, interconnect, storage, software and support included or metered separately?
- Are failed, preempted, idle, warm and maintenance periods billed?
- How are service credits calculated, claimed and applied?
- What utilization assumption underlies the quoted effective rate?
Google Cloud’s public TPU documentation says price varies by TPU version, location and consumption model. Its public page bills per chip-hour, while console usage may be displayed in VM-hours. Reconcile those units in writing before comparing an offer with another provider’s quote.
AWS AI Factories pricing is tailored to location, scale, selected accelerators and services, and existing infrastructure after a joint configuration assessment. Request a customer-specific price sheet rather than extrapolating from a public rate. Include minimum spend, term, take-or-pay, renewal, discount clawback, price-change, cancellation and termination provisions. Model the cost of revalidating the model, moving data, paying egress and operating a fallback platform if the commitment ends early.
Use comparable specifications carefully
Vendor specifications help you understand memory and scale constraints, but they are not a performance ranking. The following figures are vendor-reported 2026 specifications and should not be treated as a common-model benchmark.
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| Accelerator | Memory per chip | Memory bandwidth per chip | Pod scale | Source and qualification |
|---|---|---|---|---|
| Google TPU7x (Ironwood) | 192 GiB | 7,380 GB/s | 9,216 chips | Google Cloud documentation, 2026; specification only |
| Google TPU v6e (Trillium) | 32 GiB | 1,638 GB/s | 256 chips | Google Cloud documentation, 2026; specification only |
| Google TPU v5p | 95 GiB | 2,765 GB/s | 8,960 chips | Google Cloud documentation, 2026; specification only |
| AWS Trainium3 | 144 GB HBM3e | 4.9 TB/s | Not stated in the cited product material | AWS product page accessed 2026; specification only |
Memory capacity and bandwidth matter only in relation to your model’s weights, activations, optimizer state, sequence length and communication pattern. Ask for measured scaling efficiency at the exact chip count you plan to reserve.
Test whether the workload fits the architecture
Google’s TPU guidance says selection depends on computational characteristics, memory and scaling needs. It also notes that TPUs may be a poor fit for workloads with frequent branching, CPU-dependent custom operators or FP64 requirements. Treat those as architecture-specific screening questions, not universal rules. Profile your own graph to find control flow, host callbacks, unsupported operators and precision requirements before signing.
For each candidate, record the fraction of execution on the custom accelerator, CPU and other devices. A nominally cheap chip can lose its advantage when data repeatedly crosses device boundaries or when a small unsupported operation serializes a large batch.
Contract for operations, recovery and support
Decide who is responsible when a chip, host, fabric, compiler or managed service fails. Put the responsibility matrix next to the commercial terms.
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Questions about reliability
- Which service-level agreement applies to the exact accelerator and instance configuration?
- Does it cover instance availability, reserved-capacity delivery, networking, storage and the managed endpoint, or only one component?
- What measurement interval, exclusions, claim deadline, evidence burden and remedy apply?
- Are credits limited to future service, and do they compensate for missed training deadlines?
- What support response and escalation times are guaranteed for hardware, fabric, compiler and runtime incidents?
AWS says AI Factories services have service-specific SLAs; obtain the commitments for each service in your design rather than relying on a generic cloud SLA. Do not assume an availability credit guarantees that a multi-day training job will finish.
Assign maintenance and restart duties
Google’s default TPU capacity is Google-managed. In its All Capacity mode for specified versions, customers receive full use of reserved capacity and topology visibility while taking responsibility for hardware maintenance and failure recovery. Ask exactly which operating mode applies, who schedules maintenance, how much notice is provided, and who supplies checkpointing, retry and disaster-recovery automation.
Require observability at the job level: accelerator counters, communication metrics, thermal or power signals where available, compiler diagnostics, queue time and detailed billing records. Define the evidence needed to prove a missed SLO and the process for recovering a failed or preempted job.
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Include deployment lead time and refresh risk
If your design involves a dedicated AI Factory deployment, AWS’s current FAQ estimates approximately three to six months after the data center is ready and handed over to AWS. Treat that as a process estimate, not a guaranteed delivery date. Make site readiness, equipment delivery, acceptance testing, software bring-up and production handover explicit milestones with owners.
For every reserved generation, ask when the next generation becomes available, whether you can migrate within the commitment, and what happens if the provider retires the instance type. A refresh clause should address equivalent capacity, price changes, revalidation work and notice periods.
Use a gated procurement process
- Freeze the workload definition. Record model, data shape, precision, SLOs, quality floor, region and expected utilization.
- Issue the same test package. Give each provider identical code, representative data, framework versions and acceptance tests.
- Run a customer-controlled proof. Reproduce the provider’s benchmark and measure compilation, warm-up, steady state, retries, checkpointing and recovery.
- Score the full cost. Calculate cost per completed run or useful output at expected, low and peak utilization, including engineering and exit costs.
- Audit capacity and operations. Verify topology, quota, reservation approval, support escalation, maintenance ownership and failover region.
- Negotiate measurable terms. Attach benchmark assumptions, capacity dates, SLA definitions, remedies, refresh rights and termination provisions to the order.
- Run an exit test. Export a checkpoint and serving image, restore them on the fallback platform and measure the time and cost required.
What a defensible decision looks like
Select the provider whose written offer meets your quality and service objectives with the lowest risk-adjusted cost, not the provider with the largest memory number or the lowest advertised chip-hour. Your decision record should separate vendor claims, measurements your team reproduced and contractual guarantees. It should show why the chosen software path is maintainable, why the committed capacity will arrive where and when needed, who recovers failures, and how the business can leave if economics or availability change.
The UK Competition and Markets Authority’s final cloud-infrastructure decision, published July 31, 2025, identifies Google TPUs, AWS Trainium and Inferentia, and Microsoft Maia as examples of provider-developed AI accelerator ASICs and discusses the software needed to program them. That market context explains why software and ecosystem questions belong in the same approval package as silicon specifications; it does not establish current availability, performance or commercial terms for any particular service.
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