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Amazon AI chips are custom processors designed by Amazon Web Services (AWS) for use in its cloud infrastructure—not retail chips for installing in a personal computer. The term usually refers to Trainium, for AI training and inference, and Inferentia, for inference. Graviton is a related AWS processor family, but it is a general-purpose CPU rather than a dedicated AI accelerator.
What does “Amazon AI chips” mean?
It is an informal umbrella term for Amazon-designed silicon used in AWS cloud services. AWS makes these processors available through cloud infrastructure, including Amazon EC2 instances, rather than selling them as consumer PC components. Amazon’s overview of its custom chips and AWS’s EC2 instance documentation describe them in that cloud context.
The phrase can refer specifically to AI accelerators, or more broadly to the custom processors that support cloud and AI systems. That distinction matters: not every Amazon chip is designed to accelerate AI calculations.
How the main Amazon chip families differ
| Family | Primary role | What that means |
|---|---|---|
| Trainium | AI training and inference | Accelerates work involved in teaching models from data and running trained models. |
| Inferentia | AI inference | Designed to run trained models and generate outputs; AWS provides access through EC2 Inf instances. |
| Graviton | General-purpose cloud computing | A CPU family for broad computing tasks, including application and supporting workloads around AI systems—not a dedicated AI accelerator. |
AWS describes Trainium as part of a larger cloud system that includes chips, servers, networking, software and services. Its Trainium product page covers the accelerator, while the Inferentia product page describes inference and the EC2 Inf instance path.
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Training, inference and the role of a CPU
Training
Training is the process of teaching a machine-learning model using data. It can involve substantial computation, so specialized accelerators may be useful, depending on the model, software and scale. Trainium is AWS’s family aimed at training as well as inference workloads.
Inference
Inference means applying a trained model to produce an output—for example, generating a response or classifying an input. Inferentia is designed for this role. Trainium can also be used for inference, so the distinction is not that only one family can run a trained model; it is that AWS positions Inferentia specifically around inference.
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General-purpose computing
A CPU handles a broad range of computing tasks, while GPUs and other accelerators are suited to certain forms of parallel processing. Graviton CPUs can run general cloud workloads that support an AI application, but Graviton is not another name for Trainium or Inferentia. Amazon’s AI chip terminology explainer defines these underlying terms.
How do you use these chips?
For customers, the practical access route is an AWS cloud service, not buying a chip to install locally. Inferentia is available through EC2 Inf instances, and AWS offers Trainium through its cloud infrastructure. The specific instance, software support and availability depend on the workload and AWS offerings in the relevant region.
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For Inferentia workloads, AWS identifies its Neuron SDK as the software stack used to develop and deploy applications. Before choosing an instance, check whether the model and framework are supported, and whether the work is training, inference or a mix. Current instance options and conditions are documented in the EC2 instance types guide.
What should you compare before choosing one?
- Workload: Identify whether you need to train a model, serve predictions or run surrounding application services.
- Software compatibility: Verify that your framework, model and deployment tools work with the relevant AWS chip and software stack.
- Scale and latency: Consider throughput, response-time needs and expected demand rather than relying on a chip name alone.
- Total cost: Compare like-for-like cloud configurations for your workload. “Price-performance” means computing output for the money spent; it is meaningful only when the workload, system configuration and comparison basis are clear.
AWS publishes performance claims for its products, but those claims should be read with their stated workload and comparison in mind. They are not a guarantee that one family will be faster or cheaper for every model or deployment. The cited product information is from AWS; it does not independently establish comparative results across all workloads.
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What Amazon has reported about its custom-chip business
In a company article dated July 31, 2026, Amazon reported that its custom silicon business had exceeded a $25 billion annual revenue run rate. That is Amazon’s figure for its custom silicon business overall; it is not an independently verified figure for AI chips alone. The report appears in Amazon’s account of its chip business.
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