Intel on Compute Efficiency and the Future of AI Data Centers
As generative AI workloads push data centers into ever-higher power densities, the race for compute efficiency is more urgent than ever. Intel is staking a bold claim: an ambitious 10× energy efficiency improvement for server processors by 2030. But that goal doesn’t live in a vacuum. Behind it lie deep architectural trade-offs, new cooling paradigms, and the evolving balance between Arm and x86 in large-scale deployments.
In this Q&A, Intel’s Lynn Comp tackles these tensions head on. We explore whether energy gains in mobile SoCs map to cloud environments, what innovations are driving the 2030 target, and how enterprises can navigate power versus performance, especially as AI racks surge toward 100 kW+ densities.
Q1: Many claims are made that the Arm (Advanced RISC Machine) architecture is inherently more energy efficient than x86 in the data center, pointing to the fact that Arm is the basis of the iPhone and Apple computing clients that deliver some of the best battery life from within the phone and laptop. How much does the efficiency of a system on chip (SoC) designed for a cellphone translate into efficiency in the largest cloud scale compute instances? Do enterprises realize a benefit from the efficiency of Arm in the largest cloud instances?
A1: The Architectural (“capital A”) debate between complex instruction set computing (CISC) and reduced instruction set computing (RISC) has raged for decades. While this might be entertaining for academics or the most technical members of the press and analyst communities, the real-world efficiency of a CPU is primarily driven by micro-architectural decisions including, but not limited to: circuit design, the number of execution engines implemented, cache size, voltage/frequency operating points, process node, process optimization (low leakage or high performance) and advanced power management capabilities known as “P-states”. x86-based CPUs are used in three quarters of the enterprise server and cloud instances based on its proven ability to deliver the best combination of performance, energy efficiency, and software compatibility for the workloads that matter in today’s data centers and tomorrow’s AI factories. There are examples of highly efficient x86 client and server implementations that offer better battery life and more efficient operations than their Arm-based equivalent implementations.
In summary, the fact that a particular instruction set architecture is used widely in battery-powered consumer devices does not imply that a given system on chip design is the optimal solution to meet the demands of the modern data center. The recently formed x86 Ecosystem Advisory Group is further advancing the instruction set architecture with consistency between x86 vendors to enable faster ecosystem adoption and end-user value.
Q2: With the amount of energy consumed by data centers continually rising, Intel has set ambitious goals, including a 10x energy efficiency improvement for processors by 2030. What specific architectural innovations and design philosophies are driving Intel toward this target, and how close are you to achieving it?
A1: With the new-generation Intel® Xeon® processors with P-cores and E-cores, we continue to deliver holistic design solutions for a sustainable and efficient data center lifecycle. Intel Xeon 6 processors are equipped with power optimization features to deliver up to 7–10% power savings at 50% load, resulting in lower total cost of ownership (TCO).
To enable power and energy measurements in data centers for software developers and drive energy transparency and industry standardization, Intel Labs teamed up with National Renewable Energy Laboratory to publish an in-depth guide to measure power and energy for its applications. With the goal of enhancing data center operational efficiency, in May 2024, Intel established the community to mobilize the entire liquid cooling ecosystem and introduced its first Open IP Advanced Cooling Solutions and reference design, which prioritizes openness, ease of deployment, and scalability in response to the growing power density in data centers, cloud, and edge computing.
We worked with key customers to drive technical feasibility of single-phase immersion cooling solution (1-PIC), to support volume adoption and deployment of this novel technology in data centers. Intel is on track to meet our 2030 server product energy efficiency goals. With the Intel Xeon 6 processor products launched in 2024, we achieved 10% of the planned trajectory for our server products, a 2.85x average toward a 10x improvement by 2030.
Q3: AI workloads are dramatically changing data center power profiles, with rack densities moving from 30kW today toward 100kW+ in the coming years. What is Intel’s strategy to be part of the solution across “head node” efficiency power management technology, and efficiency of non-accelerated platforms evolving to handle these extreme density requirements while maintaining efficiency?
A3: One of the best things that can be done to improve the energy efficiency of enterprise AI deployments is to narrow the scope of each function in the workflow to what is necessary for that task and to match the model’s architecture to a given task.
For example, in an agentic workflow, an LLM may invoke a workflow, but many of the subtasks could be executed by agents that leverage more efficient SLMs or domain-specific pre-tuned models. This can reduce the amount of reasoning or token generation to get to a result within a controlled environment as well as limit the amount of data movement or I/O, which tend to be the biggest culprits in energy consumption. Said another way, while an aircraft carrier can technically cover land, sea, and air, it can’t change direction in under four nautical miles, so using an aircraft carrier when agility is required will fail at both the mission and in accomplishing tasks as efficiently as a more agile destroyer. Even NVIDIA has blogged about this dynamic, saying, “LLMs are often recognized for their general reasoning, fluency, and capacity to support open-ended dialogue. But when they’re embedded inside agents, they may not always be the most efficient or economical choice.”
Q4: How do you see enterprises tackling compute efficiency differently than a few years ago, and how does this differ from large scale cloud players?
A4: Enterprises are struggling with a conundrum on compute efficiencies when trying to add new capabilities that spike power demands when a minimum hardware configuration delivers dozens of GPUs. The promised efficiencies in AI can look more like a Rube Goldberg machine when factoring in return on investment (ROI) and utilization of an expensive dedicated asset delivering simple use cases like chatbots and RAG-enabled document processing.
Starting with free cloud credits for prototyping has been one tactic most large enterprises have used that helps avoid direct electricity bill spikes. Unfortunately, an enterprise may find the cloud-only economics turn upside down because LLM-API costs are layered on top of existing cloud spends and can be highly variable if reasoning models are employed. Agentic AI that combines different models for narrower tasks and can leverage the location of existing data repositories reduces unnecessary round trips and improves the overall efficiency in a task.
Q5. How do you see the landscape shifting from on-prem to cloud usage within an AI era, and how does this impact our collective challenge on compute efficiency?
A5: From a practical implementation standpoint in the enterprise, data has gravity and will pull compute to it since there are so many inefficiencies in moving large datasets. Companies that were ‘born in the cloud’ are likely to have to stay there, and companies with a multi-cloud hybrid environment will be unable to change their model because of where they have data. Although sovereignty isn’t entirely a compute efficiency concept, it is possible that sovereign AI or simple data sovereignty reduces compute efficiency by moving workloads out of large scale data centers, while increasing overall efficiency by limiting the amount of data movement overall.
Reference information:
For more details including test configurations, refer to: Pages 47-49 2024-2025 Intel Corporate Sustainability Report.
Customer spotlights:
Green Data Center with Moro Hub, UAE: We recently collaborated with More Hub, UAE’s innovative data center to establish a Green Data Center powered by Intel® Xeon® Processors. Check the customer story here.
Liquid Cooling technology advancements: We recently collaborated with Shell to establish industry-first certified cooling fluids for data centers, available worldwide. Check the customer story here.
As generative AI workloads push data centers into ever-higher power densities, the race for compute efficiency is more urgent than ever. Intel is staking a bold claim: an ambitious 10× energy efficiency improvement for server processors by 2030. But that goal doesn’t live in a vacuum. Behind it lie deep architectural trade-offs, new cooling paradigms, and the evolving balance between Arm and x86 in large-scale deployments.
In this Q&A, Intel’s Lynn Comp tackles these tensions head on. We explore whether energy gains in mobile SoCs map to cloud environments, what innovations are driving the 2030 target, and how enterprises can navigate power versus performance, especially as AI racks surge toward 100 kW+ densities.
Q1: Many claims are made that the Arm (Advanced RISC Machine) architecture is inherently more energy efficient than x86 in the data center, pointing to the fact that Arm is the basis of the iPhone and Apple computing clients that deliver some of the best battery life from within the phone and laptop. How much does the efficiency of a system on chip (SoC) designed for a cellphone translate into efficiency in the largest cloud scale compute instances? Do enterprises realize a benefit from the efficiency of Arm in the largest cloud instances?
A1: The Architectural (“capital A”) debate between complex instruction set computing (CISC) and reduced instruction set computing (RISC) has raged for decades. While this might be entertaining for academics or the most technical members of the press and analyst communities, the real-world efficiency of a CPU is primarily driven by micro-architectural decisions including, but not limited to: circuit design, the number of execution engines implemented, cache size, voltage/frequency operating points, process node, process optimization (low leakage or high performance) and advanced power management capabilities known as “P-states”. x86-based CPUs are used in three quarters of the enterprise server and cloud instances based on its proven ability to deliver the best combination of performance, energy efficiency, and software compatibility for the workloads that matter in today’s data centers and tomorrow’s AI factories. There are examples of highly efficient x86 client and server implementations that offer better battery life and more efficient operations than their Arm-based equivalent implementations.
In summary, the fact that a particular instruction set architecture is used widely in battery-powered consumer devices does not imply that a given system on chip design is the optimal solution to meet the demands of the modern data center. The recently formed x86 Ecosystem Advisory Group is further advancing the instruction set architecture with consistency between x86 vendors to enable faster ecosystem adoption and end-user value.
Q2: With the amount of energy consumed by data centers continually rising, Intel has set ambitious goals, including a 10x energy efficiency improvement for processors by 2030. What specific architectural innovations and design philosophies are driving Intel toward this target, and how close are you to achieving it?
A1: With the new-generation Intel® Xeon® processors with P-cores and E-cores, we continue to deliver holistic design solutions for a sustainable and efficient data center lifecycle. Intel Xeon 6 processors are equipped with power optimization features to deliver up to 7–10% power savings at 50% load, resulting in lower total cost of ownership (TCO).
To enable power and energy measurements in data centers for software developers and drive energy transparency and industry standardization, Intel Labs teamed up with National Renewable Energy Laboratory to publish an in-depth guide to measure power and energy for its applications. With the goal of enhancing data center operational efficiency, in May 2024, Intel established the community to mobilize the entire liquid cooling ecosystem and introduced its first Open IP Advanced Cooling Solutions and reference design, which prioritizes openness, ease of deployment, and scalability in response to the growing power density in data centers, cloud, and edge computing.
We worked with key customers to drive technical feasibility of single-phase immersion cooling solution (1-PIC), to support volume adoption and deployment of this novel technology in data centers. Intel is on track to meet our 2030 server product energy efficiency goals. With the Intel Xeon 6 processor products launched in 2024, we achieved 10% of the planned trajectory for our server products, a 2.85x average toward a 10x improvement by 2030.
Q3: AI workloads are dramatically changing data center power profiles, with rack densities moving from 30kW today toward 100kW+ in the coming years. What is Intel’s strategy to be part of the solution across “head node” efficiency power management technology, and efficiency of non-accelerated platforms evolving to handle these extreme density requirements while maintaining efficiency?
A3: One of the best things that can be done to improve the energy efficiency of enterprise AI deployments is to narrow the scope of each function in the workflow to what is necessary for that task and to match the model’s architecture to a given task.
For example, in an agentic workflow, an LLM may invoke a workflow, but many of the subtasks could be executed by agents that leverage more efficient SLMs or domain-specific pre-tuned models. This can reduce the amount of reasoning or token generation to get to a result within a controlled environment as well as limit the amount of data movement or I/O, which tend to be the biggest culprits in energy consumption. Said another way, while an aircraft carrier can technically cover land, sea, and air, it can’t change direction in under four nautical miles, so using an aircraft carrier when agility is required will fail at both the mission and in accomplishing tasks as efficiently as a more agile destroyer. Even NVIDIA has blogged about this dynamic, saying, “LLMs are often recognized for their general reasoning, fluency, and capacity to support open-ended dialogue. But when they’re embedded inside agents, they may not always be the most efficient or economical choice.”
Q4: How do you see enterprises tackling compute efficiency differently than a few years ago, and how does this differ from large scale cloud players?
A4: Enterprises are struggling with a conundrum on compute efficiencies when trying to add new capabilities that spike power demands when a minimum hardware configuration delivers dozens of GPUs. The promised efficiencies in AI can look more like a Rube Goldberg machine when factoring in return on investment (ROI) and utilization of an expensive dedicated asset delivering simple use cases like chatbots and RAG-enabled document processing.
Starting with free cloud credits for prototyping has been one tactic most large enterprises have used that helps avoid direct electricity bill spikes. Unfortunately, an enterprise may find the cloud-only economics turn upside down because LLM-API costs are layered on top of existing cloud spends and can be highly variable if reasoning models are employed. Agentic AI that combines different models for narrower tasks and can leverage the location of existing data repositories reduces unnecessary round trips and improves the overall efficiency in a task.
Q5. How do you see the landscape shifting from on-prem to cloud usage within an AI era, and how does this impact our collective challenge on compute efficiency?
A5: From a practical implementation standpoint in the enterprise, data has gravity and will pull compute to it since there are so many inefficiencies in moving large datasets. Companies that were ‘born in the cloud’ are likely to have to stay there, and companies with a multi-cloud hybrid environment will be unable to change their model because of where they have data. Although sovereignty isn’t entirely a compute efficiency concept, it is possible that sovereign AI or simple data sovereignty reduces compute efficiency by moving workloads out of large scale data centers, while increasing overall efficiency by limiting the amount of data movement overall.
Reference information:
For more details including test configurations, refer to: Pages 47-49 2024-2025 Intel Corporate Sustainability Report.
Customer spotlights:
Green Data Center with Moro Hub, UAE: We recently collaborated with More Hub, UAE’s innovative data center to establish a Green Data Center powered by Intel® Xeon® Processors. Check the customer story here.
Liquid Cooling technology advancements: We recently collaborated with Shell to establish industry-first certified cooling fluids for data centers, available worldwide. Check the customer story here.
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