Red Hat has announced an expansion of its collaboration with Amazon Web Services (AWS) to deliver large-scale generative AI model inference more efficiently and cost-effectively. The open-source software company aims to enable enterprises to run their AI models “on any hardware,” now also leveraging AWS-specific chips: Inferentia2 and Trainium.
The agreement is focused on a clear objective: that CIOs and infrastructure leaders can deploy generative AI into production without GPU costs becoming a barrier, nor limiting it to isolated laboratory experiments.
Generative AI yes, but without breaking the bank
The rise of generative AI has multiplied the compute needs for inference. Every corporate chatbot, internal assistant, or AI-powered search engine requires thousands or millions of low-latency requests daily. This results in increasing infrastructure bills.
Red Hat and AWS cite IDC forecasts indicating that by 2027, 40% of organizations will use custom silicon—including ARM processors and AI-specific chips—to optimize performance and costs. In this context, both companies’ strategy is clear: offer an optimized inference layer on AWS accelerators so that companies can leverage these “custom silicon” without rewriting their entire stack.
Red Hat AI Inference Server on Inferentia2 and Trainium
The first pillar of the partnership is Red Hat AI Inference Server, the company’s inference platform based on vLLM, which will be optimized to run on AWS’s AI chips: Inferentia2 and Trainium.
The promise is twofold:
- Unified inference layer for “any generative AI model,” regardless of hardware.
- Better price-performance, with Red Hat citing improvements of 30–40% over comparable GPU EC2 instances for production deployments.
In practice, this means that a company currently running models on GPUs can migrate some or all inference traffic to Inferentia2 or Trainium without changing the rest of their architecture, as long as they support this Red Hat layer.
OpenShift, Neuron, and vLLM: the technical “glue”
The second part of the announcement involves integration with Red Hat’s Kubernetes ecosystem and automation tools:
- AWS Neuron Operator for OpenShift: Red Hat collaborated with AWS to develop a Neuron operator for Red Hat OpenShift, OpenShift AI, and Red Hat OpenShift Service on AWS. This operator simplifies using AWS accelerators from Red Hat-managed Kubernetes clusters, avoiding complex manual deployments.
- Ansible and AWS automation: The company also launched the amazon.ai Certified Ansible Collection for Red Hat Ansible Automation Platform, aiming to automate AI services on AWS—from provisioning to daily operations.
- Upstream contribution to vLLM: Both Red Hat and AWS are working together to optimize an AWS AI chip plugin within the vLLM project, contributing those improvements back to the community. vLLM underpins llm-d, an open-source large language model inference project that Red Hat already integrates as a commercial feature in OpenShift AI.
The message from Red Hat is that its strategy remains “any model, on any hardware,” but with a clear emphasis now on making massive inference economical on AWS infrastructure.
Fewer tests, more production
An additional goal of this move is to help companies move out of the ongoing “proof of concept” phase with generative AI. As Techaisle notes, cited by Red Hat, the focus is shifting from experimentation to sustainable and governed operationalization of AI in production.
The combined approach of:
- container platform (OpenShift),
- inference layer (Red Hat AI Inference Server with vLLM),
- optimized accelerators (Inferentia2/Trainium),
- and automation (Ansible),
aims precisely to provide an end-to-end supported pathway for deploying, scaling, and governing models in hybrid and multicloud environments based on AWS.
Availability and roadmap
Red Hat states that the community AWS Neuron operator is already available via Red Hat OpenShift’s OperatorHub for customers using OpenShift or Red Hat OpenShift Service on AWS.
The Red Hat AI Inference Server support for AWS AI chips is initially planned as a developer preview in January 2026. This will allow technical teams to start testing integration with their own models before the feature matures into full production support.
Meanwhile, Red Hat is leveraging its presence at AWS re:Invent 2025 to reinforce its “open hybrid cloud” message tailored for the AI era: same principles of open source and portability, with a focus now on controlling inference costs per query.
Frequently Asked Questions about the Red Hat – AWS partnership for Generative AI
What does a company gain by using Red Hat AI Inference Server with AWS AI chips?
Primarily, improved price-performance for large-scale inference of generative models. Red Hat’s layer allows utilizing Inferentia2 and Trainium with a common API, maintaining flexibility to use different models without being locked into a single hardware architecture.
How does this differ from directly using GPUs on AWS?
Red Hat and AWS promise up to a 30–40% better price-performance ratio compared to comparable GPU EC2 instances, thanks to specialized chips and an optimized software stack (vLLM, Neuron, OpenShift). For inference workloads with many requests, this can translate into significant savings.
Is it necessary to use Red Hat OpenShift to benefit from this partnership?
Not always, but OpenShift greatly simplifies deployment: the AWS Neuron operator for OpenShift and OpenShift AI facilitate automated deployment, scaling, and observability of models on AWS accelerators. Without OpenShift, some of the technology can still be used, but the integrated platform experience is lost.
Is this strategy only for AWS public cloud?
The announcement focuses on AWS, its AI chips, and managed services around OpenShift on that cloud. However, Red Hat advocates for hybrid cloud: the same platform and inference logic can be extended to other environments, even if AWS-specific chips are not available.
via: redhat