AI diffusion situation… the key to victory or defeat is not 'GPU competition,' but cost-effective inference infrastructure

As enterprises introduce artificial intelligence (AI) beyond the experimental stage and move into full-scale deployment, the key to market success is also changing. Today, the core of competition is no longer simply stacking larger models and more graphics processing units (GPUs), but rather who can better build “scalable AI inference systems” that operate stably while controlling budgets.

Red Hat and Intel are responding to this trend by accelerating the expansion of AI inference infrastructure based on open-source technologies. Taneem Ibrahim, Head of AI Inference Engineering at Red Hat, and Bill Pearson, Vice President of Data Center and AI Division at Intel, pointed out at the “Red Hat Summit 2026” that the practical challenges of large-scale AI service operations lie in cost-effectiveness and optimization of infrastructure combinations.

Shifting from a single GPU approach to a CPU parallel strategy

In the early stages of generative AI diffusion, following the rise of ChatGPT and open-weight models, the mainstream approach was to deploy large models on massive GPU clusters as much as possible. However, in real enterprise environments, operational costs and controllability have become equally important as performance. At this point, how to efficiently scale models on platforms like Red Hat Enterprise Linux (RHEL) and OpenShift has become a key issue.

Ibrahim stated that Red Hat is increasingly thinking about how to operate one of its most contributed open-source projects, “vLLM,” in large-scale environments. He pointed out that the core challenge is to reduce the “cost per token” so that AI can be applied to real business scenarios, while maintaining governance capabilities and achieving large-scale deployment.

Recently, the priority of infrastructure is also changing. Pearson explained that, unlike the initial application phase centered on GPUs, with the spread of “agent-based AI,” the role of central processing units (CPUs) is once again highlighted. This means not all AI tasks require GPUs; depending on the type of workload, a reasonable combination of CPUs and GPUs becomes more important.

Red Hat and Intel expand support for vLLM based on Xeon

Based on this judgment, both companies integrated full support for vLLM in Intel Xeon (Xeon) environments in the “Red Hat AI 3.4” version. The core is not to adopt a “one-size-fits-all” configuration for all customers, but to design hardware and software combinations based on each enterprise’s business nature and expected outcomes.

Pearson analyzed that many enterprises previously adopted a GPU-centric approach with a “hammer in hand, everything looks like a nail” mindset. But he explained that by reassessing the CPU resources already deployed at data centers and shifting to on-demand GPU addition, they can achieve better performance and lower costs simultaneously.

In particular, tasks like tool invocation and data orchestration in agent-based AI workloads can often be handled without GPUs. Intel believes that letting CPUs handle such inference tasks can allow GPUs to focus on more intensive computations, thereby improving overall system efficiency.

AI infrastructure competition: “Operational efficiency” increasingly more important than “performance”

This discussion indicates that the AI market has now moved beyond pure model performance competition to an economic competition during the operational phase. For enterprises, rather than simply acquiring the most powerful hardware, how to better utilize existing data center assets while achieving “low token costs” and stable services has become a more practical challenge.

Ultimately, the winner of next-generation AI competition is likely not the company with the most powerful hardware, but the one that can maximize “cost-performance ratio” through an appropriate CPU-GPU combination and open-source software. The collaboration between Red Hat and Intel is also seen as a move to align with this market trend.

TP AI Tip: This article is summarized based on the language model of TokenPost.ai. The main content may be incomplete or inconsistent with actual facts.