Red Hat AI Inference Server

Learn how to improve the performance of your vLLM deployments with a diagnostic workflow that isolates latency issues and server saturation. Discover the key metrics to monitor and techniques to alleviate memory pressure.

Learn how to run OpenAI's Whisper model through vLLM on Apple Silicon, giving you an OpenAI-compatible endpoint on localhost. Then, discover how to take this architecture into production using Red Hat AI Inference Server.

Learn how to estimate memory requirements for your LLM fine-tuning experiments using Red Hat Training Hub's memory_estimator.py API. This guide covers the memory components, adjusting training setups for specific GPU specifications, and using the memory estimator in your code. Streamline your model fine-tuning process with runtime estimates and automated hyperparameter suggestions.

Learn how to deploy and test an Earth and space model inference service on Red Hat AI Inference Server and Red Hat OpenShift AI. This article includes two self-contained activities, one deploying Prithvi using a traditional Deployment object and another serving the model using KServe and observing Knative scaling.

Understand the PyTorch autograd engine internals to debug gradient flows. Learn about computational graphs, saved tensors, and performance optimization techniques.

Optimize vLLM performance with practical tuning tips. Learn how to use GuideLLM for benchmarking, adjust GPU ratios, and maximize KV cache to improve throughput.

Learn how to design agentic workflows, and how the Red Hat AI portfolio supports production-ready agentic systems across the hybrid cloud.

See how to use Apache Camel to turn LLMs into reliable text-processing engines for generative parsing, semantic routing, and "air-gapped" database querying.

Learn about NVFP4, a 4-bit floating-point format for high-performance inference on modern GPUs that can deliver near-baseline accuracy at large scale.

Explore how Red Hat OpenShift AI uses LLM-generated summaries to distill product reviews into a form users can quickly process.

Learn how to build AI-enabled applications for product recommendations, semantic product search, and automated product review summarization with OpenShift AI.

Deploy an Oracle SQLcl MCP server on an OpenShift cluster and use it with the OpenShift AI platform in this AI quickstart.

Explore the latest release of LLM Compressor, featuring attention quantization, MXFP4 support, AutoRound quantization modifier, and more.

This article compares the performance of llm-d, Red Hat's distributed LLM inference solution, with a traditional deployment of vLLM using naive load balancing.

Whether you're just getting started with artificial intelligence or looking to deepen your knowledge, our hands-on tutorials will help you unlock the potential of AI while leveraging Red Hat's enterprise-grade solutions.

Take a look back at Red Hat Developer's most popular articles of 2025, covering AI coding practices, agentic systems, advanced Linux networking, and more.

Discover 2025's leading open models, including Kimi K2 and DeepSeek. Learn how these models are transforming AI applications and how you can start using them.

Run the latest Mistral Large 3 and Ministral 3 models on vLLM with Red Hat AI, providing day 0 access for immediate experimentation and deployment.

Use SDG Hub to generate high-quality synthetic data for your AI models. This guide provides a full, copy-pasteable Jupyter Notebook for practitioners.

Move larger models from code to production faster with an enterprise-grade