The open source engine driving AI from experiment to production and why inference is everything

The open source engine driving AI from experiment to production and why inference is everything The open source answer to the inference challenge Hardening community innovation for the enterprise The essential open model for AI About the author Brian Stevens More like this Blog post Blog post Original podcast Original podcast Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share This blog is adapted from a recent conversation I had with University of California, Berkeley’s Ion Stoica, featured in Red Hat Research Quarterly’s article, From silos to startups: Why universities must be a part of industry’s AI growth. Read our full conversation here. For the last several years, the narrative around artificial intelligence (AI) has been dominated by large language models (LLMs) and the monumental effort of training them. The technology industry has been focused on the discovery phase—but that era is rapidly shifting. The conversation is moving from, "How do we build the model?" to, "How do we actually run the model in production at scale?" This shift is more than a technical detail; it’s the new center of gravity for enterprise AI. When AI leaves the research lab and becomes a core business capability, the focus lands squarely on inference—the firing synapses in a trained model’s “brain” before it generates an answer or takes action. And in the enterprise, inference must be fast, cost-effective, and fully controlled. Moving AI from a proof-of-concept into a reliable, production-grade service introduces significant complexity, cost, and control challenges for IT leaders. Firstly, the hardware required to run these models—especially at the scale the enterprise needs—is expensive and often scarce. Secondly, demand is unpredictable. You might have bursts of high usage followed by long periods of low activity, which can be compounded across hundreds of variants of domain-purposed models. This variability makes it extremely difficult to maximize resource utilization and protect those critical investments.