Red Hat and AWS teamed up to run generative AI workloads on custom Amazon silicon like Inferentia2 and Trainium3 instead of relying purely on Nvidia GPUs. The setup uses Red Hat AI Inference Server with vLLM optimization to handle any model while cutting costs by 30 to 40 percent compared to GPU-based EC2 instances. Red Hat also built an AWS Neuron operator for OpenShift to make deploying AI stuff on AWS accelerators way less painful.
The partnership targets companies trying to scale inference without blowing their budgets on hardware, and IDC says 40 percent of orgs will be running custom chips by 2027 anyway. Red Hat threw together an Ansible collection for easier orchestration, and they are contributing upstream fixes to vLLM since they are the biggest commercial backer of that project. The whole thing lets enterprises run high-performance AI across hybrid cloud setups without getting locked into specific chipsets.
The partnership targets companies trying to scale inference without blowing their budgets on hardware, and IDC says 40 percent of orgs will be running custom chips by 2027 anyway. Red Hat threw together an Ansible collection for easier orchestration, and they are contributing upstream fixes to vLLM since they are the biggest commercial backer of that project. The whole thing lets enterprises run high-performance AI across hybrid cloud setups without getting locked into specific chipsets.
