From hype to confidence: What makes a model “validated” for enterprise AI

We’re past the honeymoon phase of generative AI (gen AI). Most enterprises aren’t asking whether or not to use large language models (LLMs), they’re asking how and which ones. But in an ocean of flashy demos and leaderboard wins, what does it really mean for an AI model to be suitable for enterprise use?

This post explores what “validated” means in relation to the enterprise gen AI stack, and how Red Hat’s approach is helping teams select the right AI models for their needs.

The problem: AI validation is broken

Today, most organizations “validate” their AI models through a fragmented set of methods:

• Checking open source leaderboards (e.g. Chatbot Arena, Artificial Analysis)
• Running internal tests with ad-hoc scripts on a handful of curated prompts
• Estimating usage and cost/hardware requirement with back-of-the-envelope calculations

Each of these provides some information. but none of them give the full picture. Taken in isolation they often mislead teams into thinking an AI model is ready for production environments when it’s not.

The result? AI applications break or become unmanageable at scale and organizations get stuck in the proof of concept (POC) phase forever. This leads them into spiraling inference costs, latency rising when the load increases or unexpected model alignment issues—all of which could have been identified in advance.

True AI model validation isn’t a one-time benchmark. It’s a structured process grounded in real-world constraints.

Defining “validation” in the context of enterprise AI

AI model validation is the process used by data scientists to test a model's accuracy under real-world operational loads. A more comprehensive approach for enterprises should include assessing their AI models with real data and tasks, across diverse hardware, targeting key enterprise use cases. To help organizations move from POCs to production with confidence, AI model validation needs to be redefined around two core pillars:

1. Scalable performance: A validated model must maintain low and stable latency under concurrent user traffic and consistently meet service level objective (SLO) targets. Validation should include rigorous testing across a variety of workload scenarios and hardware configurations. It should also provide a simple way to understand the tradeoffs between performance, accuracy and cost, enabling teams to make informed, context-aware deployment decisions.
2. Reproducible accuracy: True validation requires transparent and repeatable accuracy testing. AI models should be evaluated using multiple curated and adversarial datasets, with clearly documented methodologies that allow results to be consistently reproduced across teams and over time.

Only when a model performs successfully based on these measures can it be considered truly enterprise-ready.

Red Hat’s approach to model validation

Red Hat is proud to introduce validated third-party AI models, offering confidence, predictability and flexibility when deploying these across the Red Hat AI platform.

With a growing number of foundation models, inference server configurations and hardware accelerators to choose from, identifying the right combination for a given use case is no small feat. Red Hat AI offers organizations a view into compute capacity guidance and empirical test results to help customers make informed decisions grounded in real performance data.

Tackling two key pain points

• Tradeoff clarity: Navigating the performance, accuracy and cost tradeoffs of modern AI models can feel like trying to solve a complex puzzle. Red Hat makes this easier by running workload-specific benchmarks and presenting results in a transparent, reproducible format.
• Business context: Mapping these tradeoffs to real-world enterprise use cases is essential. Red Hat helps customers understand how AI models and infrastructure decisions will affect application behavior in production.

The Red Hat AI team runs a curated set of third-party models through rigorous performance testing and accuracy evaluations across multiple hardware and configuration scenarios. This allows us to validate models that are not only performant, but also ready for deployment across Red Hat AI Inference Server, Red Hat Enterprise Linux AI and Red Hat OpenShift AI.

Validated models are marked with the “Red Hat AI validated model” badge and featured in the Red Hat AI page on Hugging Face, Red Hat AI Ecosystem Catalog and Red Hat OpenShift AI Model Catalog.

Red Hat regularly validates and tests newly released AI models to make sure they run effectively in vLLM across each product of the Red Hat AI platform, helping organizations gain quick access to the latest frontier models.

Customers can also engage with Red Hat AI experts to review results from model validation and receive tailored capacity planning guidance. These insights help teams move beyond leaderboard hype and confidently deploy the best-fit third-party models on their infrastructure of choice with full visibility into expected performance, accuracy and cost.

Learn more about validated models by Red Hat AI and start using them today for your AI deployments.