Intel Accelerates Open Infrastructure for the Agentic AI Era

As agentic workflows grow more complex, they place greater demands on general-purpose compute to orchestrate workloads, process data, manage I/O and execute tasks that keep accelerators productive. To address these needs, select Xeon 6+ configurations scale beyond 46,000 cores per rack at 133.45 kW total rack power, delivering greater CPU compute density to support agentic AI at scale.

Building on two decades of rack-scale innovation and the broad x86 software ecosystem, Xeon provides the high-density, general-purpose compute foundation needed to scale agentic AI. Working alongside GPUs and specialized accelerators, Xeon orchestrates workloads, executes tasks and manages data movement to keep the entire system productive. As AI infrastructure evolves, performance is increasingly measured by the number of concurrent agents a rack can sustain at target latency - and Xeon helps maximize that delivered work by keeping data flowing, tools executing and accelerators fed.

Intel GPU D-Series: Purpose-Built for Efficient Inference at Scale

Intel GPU D-series, code-named Crescent Island, is designed to improve the economics of sustained, memory-intensive AI inference. Built on Intel's latest Xe3P architecture, the GPU combines up to 480 GB of LPDDR5X memory, 1.5 TB/s of memory bandwidth and 1.3 petaflops of FP8 compute within a 350-watt power envelope. This balance of memory capacity, compute performance and power efficiency reflects Intel's approach to optimizing infrastructure for real-world inference workloads.

Designed for large language models, long-context workloads and concurrent inference services, Intel GPU D-series supports models with up to 300 billion parameters on a single card and more than one trillion parameters in an eight-GPU system. Target applications range from multi-tenant AI services to robotics and other latency-sensitive workloads.

With a standard PCIe form factor, air-cooled design and common Xe software foundation, Intel GPU D-series is designed to simplify deployment within existing data center environments while supporting application portability across Intel GPU platforms. This allows customers to scale inference without specialized rack or cooling infrastructure.

"Inference is becoming the defining AI workload, and enterprises need infrastructure that delivers the right performance at the right cost," said Anil Nanduri, vice president, AI Products & GTM, Intel Data Center Group. "With Intel GPU D-series, we're focused on making high-capacity inference practical to deploy and economical to scale."

To help bring these capabilities into production, Intel is collaborating with a broad ecosystem of OEMs, ODMs, neoclouds, independent software vendors, appliance providers and AI-native companies. Together, these efforts focus on validated platforms, software integration and deployment-ready solutions that reduce complexity and help customers move more quickly from model evaluation to production.