Red Hat AI

Learn how to benchmark local LLMs with Red Hat AI Inference and Red Hat Enterprise Linux by deploying vLLM and testing performance on your hardware.

Learn how hierarchical orchestration scales multi-agent systems by replacing passive merging with active coordination to prevent attention dilution.

Learn how to use Jev for bounded semantic judgments on messy text, returning typed probabilities to help you build reliable decision models.

Double Nemotron 3.5 Super VL inference throughput on NVIDIA Hopper GPUs. Learn how FP8 quantization and DSpark speculative decoding boost speed and accuracy.

Learn how to choose between vLLM and SGLang for production LLM inference by comparing PagedAttention, RadixAttention, and independent benchmark results.

Explore how Red Hat AI Inference 3.5 MaaS brings multi-tenant API governance, API key authentication, and token rate limiting to LLM endpoints on AKS.

Learn how Rossoctl manages agent security and observability on Red Hat OpenShift using A2A, SPIRE, and MLflow to prevent agent sprawl and enable zero trust AI.

Learn how to add NeMo Guardrails to LangGraph agents on Red Hat OpenShift AI using a proxy pattern to inspect requests without modifying agent code.

Compare Jev decision models with Red Hat guardrails to evaluate AI safety performance, cost, and latency for enterprise implementation.

Learn how to test AI agent skills with skill-creator and promptfoo to prevent silent misfires and ensure reliable trigger behavior in CI pipelines.

Learn how to secure LLM supply chains by implementing behavioral probing and provenance verification to detect backdoors that evade standard model scanning.

Learn how to use garak, an open source LLM vulnerability scanner developed by NVIDIA, to perform red teaming on AI models.

Implement enterprise-wide automation with Pipeline Failure Analyzer: AI-powered CI/CD failure diagnosis.

Implement structured decision reads with DiffusionGemma on Red Hat AI to achieve low-latency, self-hosted AI for regulated industries.

Learn how to reduce Llama 3.1 8B Instruct model size and improve performance with W8A8 INT8 quantization.

Automate enterprise-wide RAG pipelines with Red Hat OpenShift AI's AutoRAG, speeding up AI optimization for your business.

Manage AI infrastructure costs and governance with Red Hat AI 3.4's Model-as-a-Service.

Learn how to install and configure OpenCode, an open source AI coding assistant, for local development with Ollama, OpenVINO, or Red Hat AI.

Improve large language model inference speed with Speculators 0.6.0's FastMTP-style fine-tuning.

Cut Llama 3.1 8B VRAM by 46% without losing accuracy. Master the mechanics of INT8 W8A8 quantization, SmoothQuant, and GPTQ using llm-compressor.

Learn how to configure admission fair sharing in Red Hat build of Kueue 1.4 on OpenShift to prevent job starvation and balance shared resources cluster quotas.

Learn how P-EAGLE in Speculators v0.6.0 uses parallel drafting to reduce LLM latency. Train and deploy custom draft models with this step-by-step guide.

Learn how to reduce LLM memory footprint and costs with our guide on quantization schemes for inference engines

Headed to Devoxx Belgium 2026? Visit the Red Hat Developer booth on-site to speak to our expert technologists.

Learn how to fine-tune large language models on Red Hat OpenShift AI with Ray and Training Hub for optimized SQL generation using the LoRA algorithm.