Red Hat AI

Discover how to fine-tune large language models (LLMs) with Kubeflow Training, PyTorch FSDP, and Hugging Face SFTTrainer in OpenShift AI.

Explore how Red Hat Developer Hub and OpenShift AI work together with OpenShift to build workbenches and accelerate AI/ML development.

This article demystifies AI/ML models by explaining how they transform raw data into actionable business insights.

Learn how to build AI applications with OpenShift AI by integrating workbenches in Red Hat Developer Hub for training models (part 1 of 2).

Discover the new Llama 4 Scout and Llama 4 Maverick models from Meta, with mixture of experts architecture, early fusion multimodality, and Day 0 model support.

Discover Async-GRPO, a new library for reinforcement learning tasks that efficiently handles large models, eliminates bottlenecks, and accelerates experiments.

Discover how the adaptive SVD approach enables LLMs to continually learn and adapt without forgetting previously acquired knowledge.

Explore how RamaLama makes it easier to share data with AI models using retrieval-augmented generation (RAG), a technique for enhancing large language models.

Learning the naming conventions of large language models (LLMs) helps users select the right model for their needs.

Explore how to run tools with Node.js using Llama Stack's completions API, agent API, and support for in-line tools, local MCP tools, and remote MCP tools.

Learn how quantized vision-language models enable faster inference, lower costs, and scalable AI deployment without compromising capability.

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This article demonstrates how to fine-tune LLMs in a distributed environment with open source tools and the Kubeflow Training Operator on Red Hat OpenShift AI.

Explore inference performance improvements that help vLLM serve DeepSeek AI models more efficiently in this technical deep dive.

Visit the Red Hat Developer booth to speak with our expert technologists. Building and delivering modern, innovative apps and services is more complicated and fast-moving than ever. Red Hat Developer has the answers and expertise to help you succeed.

Podman AI Lab, which integrates with Podman Desktop, provides everything you need to start developing Node.js applications that leverage large language models.

Explore new open source quantized reasoning models based on the DeepSeek-R1-Distill suite that deliver near-perfect accuracy and inference speed improvements.

Discover Sparse Llama: A 50% pruned, GPU-optimized Llama 3.1 model with 2:4 sparsity, enabling faster, cost-effective inference without sacrificing accuracy.

Explore how vLLM's new multimodal AI inference capabilities enhance performance, scalability, and flexibility across diverse hardware platforms.

Learn about an efficient inference scaling method that can improve your model's reasoning ability and performance at runtime while saving on compute costs.

Explore multimodal model quantization in LLM Compressor, a unified library for optimizing models for deployment with vLLM.