BREAKING: Amazon Deploys Cerebras Wafer-Scale AI Chips on AWS to Turbocharge AI Models

Amazon Web Services has integrated Cerebras Systems' revolutionary wafer-scale chips into its cloud infrastructure, a move that dramatically accelerates AI inference performance for real-time applications. The partnership, announced March 14, 2026, directly addresses what AWS executives call 'the critical bottleneck' in AI deployment—speed—by deploying Cerebras' Wafer-Scale Engine technology within Amazon Bedrock, potentially reshaping competitive dynamics in the cloud AI services market.

Amazon splits prefill and decode across separate chips

AWS said the design uses a method called inference disaggregation. That means splitting AI inference into two parts. The first part is prompt processing, also called prefill. The second part is output generation, also called decode.

AWS said the two jobs behave very differently. Prefill is parallel, compute heavy, and needs moderate memory bandwidth. Decode is serial, lighter on compute, and much more dependent on memory bandwidth. Decode also takes most of the time in these cases because every output token has to be produced one by one.

That is why AWS is assigning different hardware to each stage. Trainium will handle prefill. Cerebras CS-3 will handle decode.

AWS said low-latency, high-bandwidth EFA networking will connect both sides so the system can work as one service while each processor focuses on a separate task.

David said, “What we’re building with Cerebras solves that: by splitting the inference workload across Trainium and CS-3, and connecting them with Amazon’s Elastic Fabric Adapter, each system does what it’s best at. The result will be inference that’s an order of magnitude faster and higher performance than what’s available today.”

AWS also said the service will run on the AWS Nitro System, which is the base layer for its cloud infrastructure.

That means Cerebras CS-3 systems and Trainium-powered instances are expected to operate with the same security, isolation, and consistency that AWS customers already use.

Amazon pushes Trainium harder as Nvidia faces another threat

The announcement also gives Amazon another opening to push Trainium against chips from Nvidia, AMD, and other big chip companies. AWS describes Trainium as its in-house AI chip built for scalable performance and cost efficiency across training and inference.

AWS said two major AI labs are already committed to it. Anthropic has named AWS its primary training partner and uses Trainium to train and deploy models. OpenAI will consume 2 gigawatts of Trainium capacity through AWS infrastructure for Stateful Runtime Environment, frontier models, and other advanced workloads.

AWS added that Trainium3 has seen strong adoption since its recent release, with customers across industries committing major capacity.

Cerebras is handling the decode side of the setup. AWS said CS-3 is dedicated to decoding acceleration, which gives it more room for fast output tokens. Cerebras says CS-3 is the world’s fastest AI inference system and delivers thousands of times greater memory bandwidth than the fastest GPU.

The company said reasoning models now make up a larger share of inference work and generate more tokens per request as they work through problems. Cerebras also said OpenAI, Cognition, Mistral, and others use its systems for demanding workloads, especially agentic coding.

Andrew Feldman, founder and chief executive of Cerebras Systems, said, “Partnering with AWS to build a disaggregated inference solution will bring the fastest inference to a global customer base.”

Andrew added, “Every enterprise around the world will be able to benefit from blisteringly fast inference within their existing AWS environment.”

The deal adds more pressure on Nvidia, which in December signed a $20 billion licensing agreement with Groq and plans next week to unveil a new inference system using Groq technology.

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