For three years, artificial intelligence infrastructure has been dominated by one idea: Bigger is better. Bigger models, bigger clusters, and bigger checks written to a small group of suppliers. At this week’s Open Compute Project Global Summit, Intel Corp. argued that the next phase of AI will look very different and that Intel is well-positioned to benefit.
During his keynote, Nicolas Dubé (pictured), corporate vice president at Intel, framed his OCP keynote around a simple phrase: “Put every prompt in its place.” He shared new details about the Intel GPU D-series, formerly code-named Crescent Island, as well as Xeon rack designs built largely on OCP’s Open Rack v3 spec and open software work.
Underneath is a strategic argument worth attention: Intel believes inference, not training, will define enterprise AI economics and that agentic AI will bring the CPU back to the center of the data center. If that’s right, Intel’s turnaround gets a second engine at exactly the right time.
Not every prompt needs a frontier model
Today, most organizations default to sending every request to a frontier model, whether or not the task warrants it. That’s like hiring a senior law partner to proofread a memo. Intel’s alternative is an orchestration layer that evaluates each request based on the required depth of intelligence, context length, latency, data sensitivity and sovereignty, and cost and energy.
Deep research may justify using a frontier model. Proprietary documents may need to remain within the enterprise. Routine coding, summarization or operational tasks can run on a smaller open model at a small fraction of the cost.
“Open models are expanding the options enterprises have for deploying AI,” said Dubé. “To realize their full potential, organizations need infrastructure that can match different workloads with the right intelligence, compute resources and economics.”
The momentum behind open models has never been stronger. In recent months, I’ve spoken with dozens of companies aggressively shifting to them. Intel cited Vercel AI Gateway data showing that open-weight models’ share of observed token traffic rose from roughly 31% in early July to about 78% by late September. One gateway isn’t the market, but it aligns with what I hear from IT leaders who are uneasy about runaway token bills and about sending sensitive data to someone else’s model.
Crescent Island targets the enterprise middle
The D-series, first disclosed at last year’s OCP Summit, is the hardware embodiment of that thesis. It’s built on the Xe3P architecture and delivers 1.3 petaflops of FP8 compute and 1.5 terabytes per second of memory bandwidth in a 350-watt, air-cooled PCIe form factor. The design supports up to 480 gigabytes of LPDDR5X memory. Intel’s own branded card carries 160 gigabytes, and partners can build higher-capacity cards.
The most interesting decision is the memory choice. Rather than chase Nvidia Corp. and Advanced Micro Devices Inc. with expensive, supply-constrained high-bandwidth memory, Intel chose LPDDR, trading peak bandwidth for capacity and cost. That’s the right trade for inference, where the bottleneck is often fitting the model and its context into memory. Intel says it supports models up to 300 billion parameters on a single card and more than 1 trillion in an eight-GPU system.
Just as important is what it doesn’t require. Most enterprise data centers weren’t built for liquid-cooled racks drawing 100-plus kilowatts. A 350-watt card that fits in a standard air-cooled server lets a company start with a single card, move to an eight-way server, and scale to rack-level systems only when the workload demands it. Inference is becoming the defining AI workload, and enterprises need infrastructure that makes the transition easier and more cost-effective.
Agentic AI is a CPU story
The most underappreciated part of the announcement is the Xeon rack work. Agentic AI turns inference into a multi-step workflow spanning retrieval, reasoning, tool calls, database queries and application execution. Every agent generates tokens and a significant amount of general-purpose work that runs on CPUs.
Intel showcased partner-built Xeon rack designs, many based on ORv3, with select Xeon 6+ configurations scaling beyond 46,000 cores per rack at 133.45 kilowatts. Xeon 6+ with dense, efficient cores targets high-concurrency agent workloads, while Xeon 6 with performance cores handles tasks that depend on single-thread speed. Intel argues that the right metric is no longer core count but how many concurrent agents a rack can sustain at target latency, a useful lens for buyers.
The result is a heterogeneous system: Xeon for orchestration, data processing and tool execution; the D-series for open-model inference; and SambaNova as an optional fast-token engine where speed is the priority. During his presentation, Dubé explained that Intel is tying the system together with a software architecture it’s building with the community, which’s open, downloadable today, and designed to run across the broad silicon.
Fuel for the Lip-Bu Tan rebound
All of this underscores Intel’s turnaround under Chief Executive Lip-Bu Tan, which is moving from hopeful to measurable. In the second quarter, Intel posted $16.1 billion in revenue, up 25% year-over-year, with Data Center and AI revenue up 59%, to $6.3 billion. It marked the seventh consecutive quarter of beating its own guidance. The stock has more than tripled over the past year.
It’s important to understand what’s driving growth. It isn’t Intel winning the graphics processing unit war. It’s the recognition that AI needs many central processing units. At Splunk’s .conf26 in September, Tan said Intel could meet only about half of customer CPU demand, even after Intel had already raised 2026 capital spending to more than $20 billion.
The OCP announcements extend that momentum in two ways. The agentic rack work reinforces the Xeon demand already driving the numbers, positioning Intel as an essential agent infrastructure provider rather than a legacy supplier. And Crescent Island gives Intel an accelerator play where it doesn’t have to beat Nvidia on peak performance. This could be the most practical, power-efficient way to run open models in an existing data center.
That’s the discipline Tan has brought: Stop trying to win every fight and focus on the fights where Intel has a structural advantage. Intel doesn’t need to own frontier training. It needs to own the enterprise middle, a much larger opportunity than most give it credit for.
What IT leaders should do now
The shift from scale to fit doesn’t happen to an organization. It must be planned. Here’s where I’d focus:
- Audit your prompt mix. Most organizations have limited visibility into what their AI spend is buying. Pull a few weeks of usage logs and sort requests by task type: deep reasoning, retrieval-heavy questions, coding help, summarization and classification. In my conversations with IT leaders, I often find that a large share of traffic is routine work a smaller open model could handle at a fraction of the cost. Do this before your next model contract renewal, not after.
- Treat orchestration as a strategic layer, not a feature. Routing based on cost, latency, context length and data sensitivity is where the savings and control come from. Whether you build it or buy it, the orchestration layer should be model-agnostic so that switching providers or bringing a workload in-house is a policy change rather than a re-architecture. It’s also the natural place to enforce governance, such as ensuring that regulated data never leaves your environment.
- Change how you evaluate infrastructure. Peak FLOPS and core counts are vendor metrics, not business metrics. Ask suppliers to demonstrate concurrent agents sustained at a target latency, cost per million tokens and tokens per watt on a model you plan to run. Then run the tests yourself with your own prompts and context lengths. A system that wins a benchmark at short context can look very different at the long contexts that agentic workflows generate.
- Plan for the data center you have. Liquid cooling has its place, but retrofitting facilities is expensive, slow and often constrained by available power. Before committing capital, run a proof of concept using air-cooled PCIe inference in existing racks. Start small, with one card or one server, and scale only when utilization justifies it. For many enterprise workloads, that path will be sufficient, and it is far faster to deploy.
- Don’t overlook the CPU budget. Agentic AI shifts a meaningful share of work to general-purpose compute: tool calls, data retrieval, database queries and code execution. Organizations that size only for accelerators will find their agents bottlenecked elsewhere. Model CPU demand alongside GPU demand, and account for tight supply. Intel itself says it can’t meet all current demand, so lock in capacity early.
- Insist on open standards and open software and hardware. ORv3, PCIe, and the Open Adapter Card, combined with open frameworks and orchestration layers, protect your ability to mix vendors and adapt as the market shifts. Note that OAC, while close, is not a final spec. Until then, PCIe, a proven standard, is a fine option. It’s not PCIe versus OAC but about delivering openness everywhere. Ask every supplier how quickly new open models run on its hardware and whether its optimizations live in mainstream frameworks or in a proprietary stack. Lock-in at the software layer is harder to unwind than at the hardware layer.
Final thoughts
Intel has spent much of the past few years being written off in AI, and for a while that criticism was deserved. The company chased Nvidia on Nvidia’s terms and lost. What’s different now is that Intel is playing a different game, one built around where enterprise AI is heading rather than where it has been.
The bet is that inference will be heterogeneous, that open models will continue to close the gap with proprietary ones, and that agents will make the CPU as important to AI as the GPU. None of those ideas is particularly controversial anymore, and the data increasingly supports all three. What’s still open to debate is whether Intel can execute. Crescent Island needs to ship on time and perform as promised; the software stack needs to keep pace with an open-model ecosystem that evolves weekly; and the company has to fix its supply constraints to capitalize on the Xeon demand it already has.
That’s where the Lip-Bu Tan era differs from what came before. Under Tan, Intel has been more disciplined about where it competes, more candid about what it can deliver, and more focused on customers than on roadmaps. Seven straight quarters of beating guidance don’t happen by accident. If Intel brings that same discipline to the D-series and its agentic Xeon platforms, OCP 2026 may be remembered as the moment Intel stopped trying to catch up in AI and started defining its own lane.
The first phase of AI rewarded scale. The next will reward fit. Intel’s message at OCP is that it understands the difference, and its recent results suggest the market is beginning to believe it.
Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.