Deploy with confidence: Announcing the latest Red Hat AI validated models

Deploy with confidence: Announcing the latest Red Hat AI validated models What are validated models? Red Hat’s model optimization capabilities Meet the most recent validated models Get started today Coming soon The adaptable enterprise: Why AI readiness is disruption readiness About the author Rob Greenberg More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share We are excited to introduce our most recent validated models , designed to empower your deployments. At Red Hat, our goal is to provide the confidence, predictability, and flexibility organizations need to deploy third-party gen AI models across the Red Hat AI platform. This release expands our collection of performance-benchmarked and accuracy-evaluated optimized models, helping you accelerate time to value and select the perfect fit for your enterprise use case. Red Hat AI’s validated models go beyond a simple list, providing efficient, enterprise-ready AI. We combine rigorous performance benchmarking and accuracy testing with a comprehensive packaging process designed to deploy with security and simplicity in mind. Each model is scanned for vulnerabilities and integrated into a managed software lifecycle, helping ensure you receive a high-performing and resource-optimized asset that is focused on security, easy to manage, and ready for long-term updates. The world of large language models (LLMs) is expanding rapidly, making it difficult for enterprises to choose the right one. Organizations often struggle with AI resource capacity planning and ensuring that a model's performance can be reliably reproduced. That's where Red Hat's validated models come in. We provide access to a set of ready-to-use, third-party models that run efficiently on vLLM within our platform. We simplify the selection process by performing extensive testing for you. Our model validation process includes: Performance benchmarking using GuideLLM to assess resource requirements and cost on various hardware configurations.