Empowering the AI Everywhere Enterprise: Intel® Xeon® Platform Support in Red Hat AI 3.4

Authors:

Raghu Moorthy, Principal Engineer, Intel
Erwan Gallen, Senior Principal Product Manager - Generative AI, Red Hat

Agentic AI has officially crossed the line from “interesting demo” to production reality. Modern coding agents do more than generate text; they coordinate multi-step workflows that resemble real digital coworkers. These workflows include compiling and running code, calling tools and APIs, and querying databases. That shift changes what “good AI infrastructure” looks like. It’s no longer enough to measure raw model throughput. In production, agents stress the entire inference stack: latency under load, cost per request, operational control, security boundaries, and the reliability of routing, scheduling, and tool execution all become first-class requirements.

Most organizations don’t encounter an “AI capability” problem first; they encounter an infrastructure problem. GPUs are expensive, supply can be constrained, and end-to-end procurement and integration of a GPU platform can take months. GPUs remain essential for training and for frontier-scale model inference, but as enterprises move from single-model chatbots to multi-agent systems and reinforcement loops, the host CPU takes on more responsibility. In practice, inference-heavy environments often evolve toward higher CPU-to-GPU ratios because orchestration and operational services scale with user concurrency and agent complexity.

The Case for CPU-Based AI Inference (and Why It’s Practical)

Once AI reaches production, the conversation quickly turns to token economics: inference cost, latency under load, and the ability to meet SLOs.

Here’s the key: enterprise AI is a spectrum. Not every workload requires a GPU. Many organizations deploy right-sized, open models at moderate concurrency. For those scenarios, particularly sub 20B parameter models, RAG pipelines, and agent orchestration, CPU-based inference can be both performant and operationally simpler.

To help enterprises deploy this new generation of AI systems more quickly, Intel® Xeon® processors are now supported on Red Hat AI 3.4, creating a consistent, enterprise-grade experience across CPU and GPU infrastructure. Teams can start with Intel Xeon processor-based systems they already trust, add GPUs where they truly pay off, and keep a unified operational model from development through production.

Red Hat AI 3.4 + Intel Xeon CPUs: A Unified Path to AI Production

Red Hat AI is designed to run efficiently on modern CPUs and to manage the full lifecycle of models, agents, and AI applications across hybrid cloud, on-premises, and edge environments. With Red Hat AI 3.4 supporting Intel Xeon processors as a first-class inference target, enterprises can deploy CPU-based inference with the same operational discipline they expect for GPU nodes and realize the benefits of:

• Unified lifecycle management
• Autoscaling and High Availability (HA)
• Governance controls and tight integration with OpenShift

Just as importantly, customers gain access to Red Hat AI quickstarts and Accelerators designed to help reduce setup complexity, enforce best practices, and speed up time-to-value for development teams.

Some AI quickstart examples:

• LLM serving, RAG, virtual agents
• Confidential computing-validated patterns
• Curated catalogs of validated models optimized for Intel Xeon CPU inference

All designed to help teams move from experimentation to production faster and with fewer surprises. Additionally, refer to the Intel Xeon CPUs for Red Hat OpenShift AI Reference Architecture for details on production deployments, including cluster sizing, RAG storage tiers, tuning parameters, and ISA selection guidance.

Intel® Xeon® 6 Processors: The Foundation for High Performance AI Infrastructure

Intel Xeon 6 processors include features that map directly to the demands of modern agentic and inference-centric environments, whether you’re running CPU-only inference, pairing CPUs with GPUs, or operating mixed clusters.

Intel® Advanced Matrix Extensions (Intel® AMX)

Intel AMX brings dedicated on-die matrix engines to accelerate key AI data types (BF16, FP16, INT8), delivering significant throughput gains over earlier Intel Xeon generations. Intel Xeon 6 processors with P-cores also support modern x86 AI instruction sets, such as Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX 512), and enable INT4 weight-only quantization (W4A16) to reduce memory footprint and increase model concurrency, useful when serving many users or running multiple smaller models. In agentic systems, this acceleration can support routing decisions, inline guardrails, and smaller models for tool execution.

Priority Core Turbo Technology (PCT)

In production AI systems, latency spikes often come from the control plane. Priority Core Turbo Technology (PCT) addresses this by allowing select Xeon 6 processors to dynamically prioritize a small set of high-importance cores at higher turbo frequencies. In contrast, the remaining cores run at base frequency for background work. The result is better responsiveness for latency-sensitive control-plane tasks and reduced CPU-to-GPU bottlenecks, helping the whole system maintain tighter P99 behavior.

Increased Memory Bandwidth and Capacity (DDR5 + MRDIMMs)

Host CPUs in AI systems are frequently memory-bandwidth-bound. They move lots of small objects (tokens, metadata, events), maintain in-memory indexes, and serve many concurrent users. Intel Xeon 6 processors improve responsiveness with faster DDR5 speeds (commonly up to DDR5-6400 in 1DPC configurations) and optional MRDIMMs on select platforms, which can significantly increase memory bandwidth compared to standard RDIMMs.

Higher bandwidth and capacity can also reduce the need for aggressive quantization, enable higher optimal batch sizes, and make larger context windows and KV caches more practical, especially for RAG applications.

Confidential Computing with Intel® Trust Domain Extensions (Intel® TDX)

As AI moves into regulated industries and multi-tenant environments, confidential computing becomes a differentiator. Intel TDX provides hardware-isolated virtual machines (trust domains) designed to protect sensitive data in use, even from privileged users. Intel Xeon 6 processors can also support end-to-end confidential AI patterns, including hardware-encrypted communication between confidential VMs and connected PCIe devices (such as GPUs) via a bounce buffer and, in the future, Intel® TDX Connect, helping extend protection beyond the CPU boundary.

AI Workloads where Intel Xeon Processors Excel

Many high-value production use cases align naturally with CPU strengths:

• Agentic AI: orchestration, tool execution, and state management are latency- and memory-sensitive.
• RAG pipelines: embeddings, vector search, reranking, and ingestion benefit from bandwidth and strong general compute.
• AI virtual agents: responsive multi-turn interactions without the complexity of GPU-only environments.
• Guardrails and safety models: ideal for CPU offload to reduce GPU queue contention.
• Edge AI: inference in compact server footprints with lower power draw than GPU-dense builds.
• Classical ML + deep learning: XGBoost, recommendation components, and media analytics often run best on CPU.
• Batch inference and observability: high-volume scoring, transcription, and telemetry pipelines that are latency-tolerant.

Conclusion: AI for Every Enterprise, on Every Server

The combination of Intel Xeon 6 processors and Red Hat AI 3.4 marks an important step toward making production AI more accessible and more practical for real-world enterprises. Intel Xeon 6 processors deliver built-in AI acceleration via Intel AMX, strong control-plane responsiveness via Priority Core Turbo Technology, high memory bandwidth, and confidential computing via Intel TDX. Red Hat AI 3.4 turns those capabilities into a consistent operational experience across CPU and GPU environments.

Together, Intel and Red Hat enable organizations to:

• Deploy inference, RAG, agentic workflows, and virtual agents on existing CPU infrastructure
• Reduce TCO for right-sized workloads and improve GPU utilization in mixed systems
• Scale predictably with smarter routing and cache-aware scheduling
• Accelerate time-to-value with validated models, quickstarts, and deployment guidance
• Protect sensitive workloads with hardware-based isolation and encryption

In an AI-everywhere world, the winners won’t be measured solely by the number of GPUs; they’ll be the ones with the most balanced, secure, scalable, and operationally sound platform to move AI from pilot to production.

Interested in trying it out? Red Hat partners can check out at demo.redhat.com. Customers can apply for a free Intel + Red Hat Gen AI Proof of Concept by filling out this form.

Industry Perspectives: Partner Voices

AI on Intel Xeon CPUs launches with broad ecosystem support from leading OEMs, global system integrators, and ISVs.

Intel

“The most capable AI systems aren’t GPU-only — they’re architecturally balanced, with the CPU carrying real weight in inference, orchestration, and control. Xeon 6 was built for exactly this moment, and our work with Red Hat on AI 3.4 turns that into an enterprise-ready platform organizations can deploy on infrastructure they already own.”

— Bill Pearson, VP, Data Center and Artificial Intelligence Software

Red Hat

“Enterprise AI isn’t about choosing between CPUs and GPUs — it’s about orchestrating them effectively. With Intel Xeon processors and Red Hat AI, we’re giving organizations a consistent, scalable platform to operationalize AI where it makes the most sense, unlocking performance, efficiency, and control across the entire stack.”

— Steven Huels, VP of AI Engineering & Product Strategy

IBM Fusion | OEM Partner

"IBM Fusion’s data services are purpose-built to integrate with enterprise AI pipelines. By combining IBM Fusion’s high-throughput data fabric with AI powered by Intel Xeon Processors, we enable customers to run complex, multi-source RAG workloads with incredible speed and full data sovereignty."

— Sam Werner, GM of IBM Storage

Dell Technologies | OEM Partner

“Dell PowerEdge servers have always been the backbone of enterprise compute. With the Dell PowerEdge R770 powered by Intel Xeon 6, paired with Red Hat OpenShift AI, customers now have a compelling, validated, and cost-effective path for enterprise-scale AI inference.”

— Seamus Jones, Director, Engineering Technologist, Dell Technologies

Lenovo | OEM Partner

“Technology like Intel Xeon 6 and Red Hat AI 3.4, combined with Lenovo’s ThinkSystem servers, brings our ‘AI for All’ vision to life. Customers can now deploy high-performance LLM and RAG inference across hybrid IT environments that take advantage of CPU platforms — providing greater flexibility to start and scale enterprise AI initiatives.”

— Robert Daigle, Director, Infrastructure Solutions Group, Lenovo

Supermicro | OEM Partner

“Supermicro’s X14 server systems are both thermal and power efficient to extract maximum performance from Xeon 6’s AMX acceleration. Combined with Red Hat AI 3.4, our platforms give customers the power density and operational efficiency needed to run large AI deployments at a fraction of the power cost of GPU-dense configurations.”

— Mory Lin, VP, IoT/Embedded & Edge Computing, Supermicro

Cisco | OEM Partner

“Agentic AI is shifting focus from models to applications that can reason and act in real time. At Cisco, we believe that only works when the full stack is connected, from infrastructure to application. With our deep integrations with Intel Xeon and Red Hat AI, customers can run inference and agentic workloads directly on their core Cisco infrastructure, simplifying how AI applications are deployed and operated at scale.”

— Jeremy Foster, SVP and GM, Cisco Compute

Sterling | SI Partner

"When it comes to AI, ALL resources are costly and need to be utilized as close to 100%. Not just GPUs. Xeon AI removes some of the most common blockers we encounter: AI infrastructure costs and deployment complexity. With Intel Xeon 6 and Red Hat OpenShift AI, our clients can unlock immediate ROI from AI by leveraging existing server infrastructure, while our consulting teams handle deployment, optimization, and change management.”

— Christopher Cyr, Sterling, CTO

SAS | ISV Partner

“SAS Retrieval Agent Manager (RAM) is a no-code, enterprise-grade RAG and agentic AI platform that transforms fragmented, unstructured enterprise data into governed, citation-backed AI responses. Running SAS RAM on Intel Xeon 6 with Red Hat OpenShift AI delivers the performance, on-premises data sovereignty, and operational simplicity that regulated enterprises demand. With Xeon based AI, customers in banking, insurance, healthcare, and manufacturing can deploy SAS RAM’s full agentic capabilities, including multi-agent orchestration, MCP tool integration, and real-time knowledge retrieval.”

— Jason Mann, Vice President, Internet of Things (IoT), SAS

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