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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThere is no accelerator that wins every cloud AI workload. NVIDIA GPUs are a sensible first candidate when your models change often, your software depends on GPU-specific tools, or flexibility matters. A cloud provider’s custom chip is worth a controlled trial when the model and operations are supported, the workload is stable, and capacity is available. Choose based on useful output, quality, latency, total cost, and operational fit—not peak compute figures alone. Training and inference may warrant different hardware.
What counts as a custom AI chip?
In this comparison, “custom AI chip” means an accelerator designed or offered by a cloud provider for machine-learning workloads, rather than a general-purpose NVIDIA GPU. Examples in the available provider guidance include AWS Trainium and Inferentia and Google Cloud TPUs. These are not interchangeable products: each has its own supported software path, instance configurations, availability, and workload fit.
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The decision is therefore not simply about chip architecture. You are choosing a hardware-and-software environment inside a particular cloud, with its own deployment workflow and capacity constraints. A result on one model, instance, or provider does not establish a winner for another.
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When should you start with NVIDIA GPUs?
Start your evaluation with GPUs when you value the freedom to change models or workloads, or when your team relies on GPU-first libraries, custom operations, or an established GPU development path. That is a practical starting heuristic, not a claim that GPUs are always faster or cheaper.
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Model shape and operations matter. Google’s accelerator methodology notes that tensor shapes can favor one architecture; moving to a different architecture may expose unsupported or inefficient operations, require custom kernels or specialist work, or even prompt changes to model dimensions and retraining. The more your model and tooling evolve, the more important it is to count that adaptation work alongside the chip’s runtime performance.
When is a cloud provider’s custom accelerator worth testing?
Trial a custom accelerator when you can describe the production workload clearly, the provider supports the model and required operations, and a possible cost or capacity advantage matters at your intended scale. Use the provider’s supported compiler or runtime and a production-like request mix; a favorable result on a simplified test is not enough to establish operational value.
Vendor figures can help identify candidates, but they are not substitutes for your own benchmark. Amazon CEO Andy Jassy’s 2025 shareholder letter characterized Trainium2 as having “about 30% better price-performance than comparable GPUs.” That is Amazon’s characterization; the statement does not provide enough benchmark detail to apply the figure to arbitrary models, configurations, or clouds.
AWS says Inferentia2 delivers up to 4× higher throughput and up to 10× lower latency than first-generation Inferentia. Those are AWS-stated comparisons between two Inferentia generations, not results against NVIDIA GPUs. Treat them as a reason to evaluate that product for a fitting workload, not as a cross-vendor ranking.
Compare the dimensions that determine the outcome
| Decision dimension | What to evaluate | Why it changes the choice |
|---|---|---|
| Model and software fit | Framework support, required operators and precision modes, model shape, custom kernels, and compiler/runtime path | A software mismatch can erase a hardware advantage or create porting and specialist-engineering work. |
| Quality and service target | The same model or checkpoint, quality checks, input/output distributions, context lengths, concurrency, and latency target | Throughput is meaningful only if it meets the quality and response requirements of the intended product. |
| Performance | Useful tokens or examples per second, latency distribution, time to first output where relevant, training completion time, and accelerator utilization | Peak compute figures do not capture model execution, utilization, or system behavior. |
| Full cost | Accelerator and host charges, storage and network, idle capacity, retries, engineering effort, and migration work | The useful comparison is cost per output that meets requirements, or cost per completed training job—not an hourly accelerator price alone. |
| Scale-out and recovery | Interconnect and data movement, parallelism efficiency, checkpointing, failure recovery, and scheduler behavior | Large training jobs depend on communication and operational behavior as well as compute. |
| Capacity and location | Region, quota, reservation, lead time, instance generation, and contract or commitment terms | A suitable accelerator is not a practical option if it cannot be provisioned where and when the workload needs it. |
| Operational fit | Monitoring, deployment workflow, software maturity, team experience, and portability needs | Engineering and maintenance costs can outweigh a theoretical runtime or price advantage. |
Evaluate training and inference separately
Training
For training, compare the time to finish the actual job, not just a short compute run. Include data-pipeline throughput, distributed scaling, communication, checkpointing, recovery after failures, and whether the full cluster can be obtained for the required run. Training performance does not predict inference performance: the execution pattern and service requirements differ.
Inference
For inference, compare cost per useful token or other output at the required quality and latency. Record prompt and output lengths, context distribution, batch size, concurrency, and utilization. A high throughput result that depends on batching that your service cannot use—or that misses its latency target—does not represent useful production capacity.
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NVIDIA’s benchmarking guidance identifies cost per token as a useful inference metric, but any result it presents is tied to its named configuration and is NVIDIA’s presentation of those results. It should inform a test design, not serve as a universal price comparison.
Mixed workloads
It can be reasonable to train or fine-tune on one hardware path and serve on another. Microsoft’s guidance recommends evaluating training and inference independently. The benefit of specialization has to exceed the added operational burden of maintaining multiple software and deployment paths.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a benchmark that resembles the real workload
- Define the workload. Select a representative model version and checkpoint. Specify input and output distributions, context lengths, concurrency, and the quality checks that every candidate must pass.
- Record the implementation. For each candidate, document the supported framework, compiler or runtime, precision, parallelism, instance shape, and relevant software versions. Use the provider-supported path rather than an artificial configuration.
- Measure service-level results. For inference, capture steady-state throughput, latency distribution, time to first output where relevant, and accelerator utilization. For training, capture job completion time and distributed behavior.
- Include end-to-end overhead. Account for warm-up and compilation, data movement, storage, networking, orchestration, and realistic idle or burst periods. Separate one-time porting costs from recurring operating costs.
- Calculate the result that matters. Work out cost per useful output at the required service level, or total cost per completed training job. State the region, pricing basis and date, capacity assumptions, and any reservation or commitment terms.
- Repeat and disclose. Repeat enough runs to understand variation and report the tested configuration and limits. Do not project a result from one model or vendor figure onto other workloads.
This whole-system approach is consistent with AWS Well-Architected performance-efficiency guidance, which recommends using purpose-built hardware specific to a machine-learning workload, including Trainium, Inferentia, and Amazon EC2 DL1. It is workload-selection guidance from AWS, not neutral evidence that an AWS accelerator beats another provider’s option.
How to interpret published performance claims
Published figures are useful for shortlisting, but check exactly what was compared. For example, a Google Cloud blog based on MLPerf Inference v3.1 results reported that Cloud TPU v5e delivered 2.7× performance per dollar versus TPU v4 on a specified GPT-J benchmark. Google said its derived performance-per-dollar measure was not an official MLPerf metric and depended on prices current when the blog was published. It is historical context for that benchmark—not a current GPU-versus-TPU price comparison.
Keep each claim within its stated scope: a comparison between product generations is not a comparison with a different chip family, and a vendor’s price-performance characterization is not a general result across models, regions, or configurations. For your decision, the comparable evidence is a repeatable run of your workload at your quality and service targets, using the software and capacity you can actually deploy.
Quick Recap
Make the shortlist, then test it
- GPU-first evaluation: Choose this starting path if model experimentation, GPU-dependent software, or custom operations are central to the workload.
- Custom-accelerator trial: Add a provider’s option when model and operator support are established, the workload is well characterized, and capacity can meet the schedule.
- Separate paths by job: Consider distinct training, fine-tuning, and serving hardware only when measured benefits justify added operational complexity.
- Defer a choice if necessary: If required capacity, supported operations, or a representative benchmark is missing, the available evidence is not enough to make a defensible performance or cost decision.
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