---
title: "Red Hat AI"
description: "2 posts about Red Hat AI, summarised, each linking to the original."
---

# Red Hat AI
> 2 posts about Red Hat AI, summarised, each linking to the original.

## Articles

### [Red Hat AI Inference 3.5 MaaS governance on AKS](https://yomu.fyi/post/red-hat-ai-inference-3-5-maas-governance-on-aks.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: clobner
- Published: Oct 13, 2026
- Type: Tutorial

Red Hat AI Inference 3.5 extends its Model-as-a-Service (MaaS) governance layer to non-OpenShift Kubernetes distributions, including Azure Kubernetes Service (AKS). MaaS adds multi-tenant API governance on top of the inference platform, controlling authentication, rate limiting, and model access via Authorino and Limitador from Red Hat Connectivity Link (Kuadrant). A model is first deployed with a single LLMInferenceService YAML, creating a vLLM server and router-scheduler, but the endpoint is open. Applying a MaaSSubscription and MaaSAuthPolicy activates governance: Authorino validates API keys or JWTs, and Limitador enforces token rate limits. The post demonstrates three enforcement scenarios on an AKS cluster with a T4 GPU running Qwen3-0.6B: 401 for missing or invalid keys, 200 for authenticated requests, and 429 when rate limits are exceeded, blocking requests before they reach vLLM.


### [Run decision models on vLLM and Red Hat AI using DiffusionGemma](https://yomu.fyi/post/run-decision-models-on-vllm-and-red-hat-ai-using-diffusiongemma.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Lucas Wilkinson, Rob Greenberg
- Published: Sep 28, 2026
- Type: Explainer

A new category of AI model called ‘System One’ makes fast, typed, probabilistic decisions from unstructured input, exemplified by TypeSafe AI’s Jev, which offers guaranteed structure, default probabilities, and low latency (70–500 ms) for high-volume decisions like routing and moderation. Jev is a hosted API without self-hosting options, prompting the vLLM community to adapt DiffusionGemma, an open block-diffusion model, as a self-hostable decision engine. The approach uses a fixed-length canvas seeded with answer templates, a single denoising step, and calibrated logprobs to return decisions with confidence scores, achieving over 300 decisions per second on a Cloud Run deployment. DiffusionGemma is already validated on Red Hat AI, and the structured-read support in vLLM nightly enables teams to run decision models on their own infrastructure. The post provides a recipe using vLLM nightly and a decision server that exposes a Jev-compatible endpoint for prototyping.