Right-Sizing AI: Small Language Models for Real-World Production

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Sep 20, 2025

In this discussion, Steven Huels, VP of AI Engineering at Red Hat, unpacks the power of small language models (SLMs) for real-world applications. He highlights the advantages of SLMs in fitting onto single enterprise GPUs and their operational capabilities. The conversation dives into self-hosting models versus relying on APIs, tackles organizational readiness, and discusses innovations in agentic systems. Steven shares real-world examples like scam detection and emphasizes the importance of customization, automated evaluation, and continuous retraining for efficient AI deployment.

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ADVICE

Match Hosting To Operational Maturity

Evaluate whether your IT organization already runs platforms before self-hosting models.
If not, consider an integrated AI platform to extend existing operational skills and reduce maintenance burden.

INSIGHT

Model Selection Driven By Constraints And Pace

Model choice depends on geopolitical, popularity, and use-case constraints more than raw hype.
Enterprises test multiple models with custom evaluation suites and must cope with rapid model churn.

INSIGHT

Agentic Systems Are Becoming Service-Oriented

Agentic systems trend toward a service-oriented architecture of specialized agents coordinating to solve complex tasks.
The ensemble of smaller, task-specific agents can outperform a single general-purpose model for cost and fit.

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Summary
In this episode of the AI Engineering Podcast Steven Huels, Vice President of AI Engineering & Product Strategy at Red Hat, talks about the practical applications of small language models (SLMs) for production workloads. He discusses how SLMs offer a pragmatic choice due to their ability to fit on single enterprise GPUs and provide model selection trade-offs. The conversation covers self-hosting vs using API providers, organizational capabilities needed for running production-grade LLMs, and the importance of guardrails and automated evaluation at scale. They also explore the rise of agentic systems and service-oriented approaches powered by smaller models, highlighting advances in customization and deployment strategies. Steven shares real-world examples and looks to the future of agent cataloging, continuous retraining, and resource efficiency in AI engineering.

Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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Your host is Tobias Macey and today I'm interviewing Steven Huels about the benefits of small language models for production workloads

Interview

Introduction
How did you get involved in machine learning?
Language models are available in a wide range of sizes, measured both in terms of parameters and disk space. What are your heuristics for deciding what qualifies as a "small" vs. "large" language model?
What are the corresponding heuristics for when to use a small vs. large model?
The predominant use case for small models is in self-hosted contexts, which requires a certain amount of organizational sophistication. What are some helpful questions to ask yourself when determining whether to implement a model-serving stack vs. relying on hosted options?
What are some examples of "small" models that you have seen used effectively?
The buzzword right now is "agentic" for AI driven workloads. How do small models fit in the context of agent-based workloads?
- When and where should you rely on larger models?
When speaking of small models, one of the common requirements for making them truly useful is to fine-tune them for your problem domain and organizational data. How has the complexity and difficulty of that operation changed over the past ~2 years?
Serving models requires several operational capabilities beyond the raw inference serving. What are the other infrastructure and organizational investments that teams should be aware of as they embark on that path?
What are the most interesting, innovative, or unexpected ways that you have seen small language models used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on operationalizing inference and model customization?
When is a small or self-hosted language model the wrong choice?
What are your predictions for the near future of small language model capabilities/availability?

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Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0