Executive Summary

Custom CPU development is essential for maximizing the efficiency and performance of accelerated computing architectures. NVIDIA’s strategic approach to CPU design, specifically its purpose-built Arm-based CPUs, delivers significant competitive advantages by addressing the limitations of general-purpose CPUs in GPU-accelerated environments. This report analyzes how this strategy delivers competitive advantages through improved performance, power efficiency, and system integration while supporting broader ecosystem compatibility.

Introduction: The Critical Role of CPUs in Accelerated Computing

The rapid advancement of artificial intelligence and high-performance computing has dramatically shifted data center architecture requirements. While GPUs and specialized accelerators have received significant attention, CPUs remain fundamental components in these systems. However, the relationship between CPUs and accelerators requires strategic reconsideration as data centers increasingly prioritize acceleration-first approaches.

The Inefficiency Challenge of General-Purpose CPUs

General-purpose CPUs present several significant challenges in acceleration-focused environments:

1. Architectural Misalignment: Traditional CPUs are designed for broad workload versatility rather than optimized acceleration support. This creates fundamental inefficiencies when paired with specialized accelerators like GPUs.
2. Power Consumption Imbalance: In GPU-intensive environments, suboptimal CPU power utilization diverts critical energy resources from acceleration tasks, reducing overall system efficiency.
3. Performance Bottlenecks: General-purpose CPUs can become limiting factors in data movement and processing pipelines, constraining the full potential of connected accelerators.
4. Optimization Trade-offs: When deployed within acceleration-heavy data centers, conventional CPUs maintain optimizations for average workloads rather than specifically supporting AI services and related computational tasks.

These factors collectively create substantial performance and efficiency gaps that impact the total cost of ownership (TCO) and computational capability in accelerated computing environments.

For example, in collaboration with Ansys, NVIDIA highlighted the critical efficiency limitations of general-purpose CPUs within power-constrained accelerated computing environments. While raw performance might appear similar, the underlying power demands differ significantly. Benchmark results from Ansys LS-DYNA, a CPU-intensive crash simulation workload, illustrate this point. Leading x86 CPUs, like the Intel Xeon Platinum 8480+ (112 cores) and AMD EPYC 9654 (192 cores), delivered a competitive performance to NVIDIA’s Grace CPU Superchip (144 cores) on both the car2car_20m and odm_10m models. However, this comparable performance masked a crucial difference: the Grace CPU achieved this with over a 2x improvement in performance-per-watt. In practical terms, this means a data center using general-purpose CPUs could be forced to limit computational throughput by as much as half to stay within the same power budget, effectively negating any perceived performance parity. This underscores the inherent inefficiencies of deploying general-purpose CPUs, designed for broad application, in specialized, power-sensitive accelerated computing roles.

The NVIDIA Grace CPU provides superior energy efficiency compared to a range of x86 architectures for this Ansys computer-aided engineering crash analysis.

NVIDIA’s Strategic Imperative for Custom CPU Development

NVIDIA’s pursuit of custom CPU development addresses specific technical requirements essential for next-generation accelerated computing:

Single-Thread Performance Optimization

While parallel processing dominates accelerated computing, Amdahl’s Law underscores that sequential processing segments ultimately limit overall application performance. By optimizing single-thread performance, NVIDIA targets these critical bottlenecks to enhance system-wide acceleration potential. This approach recognizes that as more workload components become accelerated, remaining non-parallel segments become increasingly significant constraints.

Memory and Interconnect Optimization

Data movement is a critical bottleneck in AI and HPC workloads, necessitating high-bandwidth, low-latency communication between CPUs, GPUs, and memory. Traditional architectures rely on PCIe for CPU-GPU communication, which introduces higher latency and bandwidth constraints. NVIDIA’s custom Grace CPU addresses these inefficiencies by integrating LPDDR5X memory with up to 1 TB/s bandwidth, reducing memory contention and preventing GPU stalls. Unlike generic x86 architectures, the Arm-based Grace CPU is designed for coherent memory sharing, ensuring GPUs have seamless access to CPU memory without unnecessary data duplication.

A key advantage of custom CPU design is deeper integration with proprietary interconnects like NVLink-C2C (Chip-to-Chip), which extends beyond PCIe’s limitations. NVLink-C2C provides direct cache coherency, allowing the CPU and GPU to access shared memory with lower latency while maintaining data consistency. This architecture reduces redundant memory copies, optimizes power efficiency, and increases overall throughput—critical for large-scale AI models and scientific simulations. Unlike standard off-the-shelf solutions, NVIDIA’s tight CPU-GPU co-design ensures hardware-level optimizations that accelerate workloads by minimizing memory bottlenecks and improving data locality.

By leveraging a custom CPU, advanced interconnects, and optimized memory hierarchy, NVIDIA achieves higher computational efficiency, reduced inter-node communication overhead, and more deterministic performance in AI and HPC workloads.

In traditional architectures, the CPU and GPU have distinct, separate memory spaces. This means that data stored in CPU memory (RAM) is not directly accessible to the GPU, and vice versa. Grace Hopper uses NVLink-C2C for coherent memory sharing.

Advanced Power Management

During AI training and inference, GPUs handle the bulk of computation, while CPUs manage orchestration, data preprocessing, and I/O—often operating below full capacity. Power steering dynamically reallocates power from underutilized CPU cores to GPUs, enabling higher GPU clock speeds and thus more computations per second. This accelerates model training, reduces inference latency, and enhances TCO. By intelligently managing power, AI data centers can unlock more GPU performance, maintain system balance, and minimize wasted power, ultimately driving greater efficiency and cost-effectiveness—without exceeding thermal or power constraints.

NVIDIA’s Automatic Power Steering.  Left: In a traditional system with a static power budget, the CPU often consumes a significant portion of the available power, even when underutilized in GPU-accelerated workloads. Right: Power steering dynamically reallocates power from the CPU to the GPU when the GPU has a higher computational demand, maximizing overall system performance and efficiency within the same power envelope.

The Strategic Value of Arm Architecture

NVIDIA’s selection of Arm’s  platform for its custom CPU development offers multiple strategic advantages:

1. Industry-Leading Efficiency: Arm’s architectural and implementation approach delivers superior performance-per-watt metrics, addressing critical power constraints in modern data centers.
2. Customization Flexibility: Unlike fixed CPU designs, Arm’s licensing model permits extensive silicon and platform customization, enabling NVIDIA to implement acceleration-specific optimizations.
3. Established Software Ecosystem: By leveraging Arm’s growing data center presence, NVIDIA maintains compatibility with existing software stacks while introducing custom performance enhancements.

This approach allows NVIDIA to develop deeply tailored system architectures for accelerated computing while maintaining compatibility across a diverse computing landscape from cloud deployments to on-premises installations and client devices.

Competitive Advantages and Market Implications

NVIDIA’s custom CPU strategy creates substantial competitive differentiation through several key mechanisms:

1. Silicon Customization: By developing CPU designs specifically optimized for AI-first datacenters, NVIDIA can deliver system performance and efficiency gains beyond what general-purpose host CPU alternatives offer.
2. Superior Resource Utilization: Custom CPU design enables better performance-per-watt metrics through acceleration-specific optimizations impossible with general-purpose processors.
3. Developer Productivity Enhancements: Simplified memory management and improved workload efficiency reduce development complexity while maintaining Arm ecosystem compatibility and workload portability.
4. Legacy Constraint Elimination: Breaking free from traditional CPU design limitations opens new possibilities for AI and HPC applications while maintaining value for CPU-only workloads through high single-thread performance, enhanced bandwidth, and optimized fabric connectivity.

System Optimization

The AI and HPC-driven data center is shifting from a focus on individual component performance to full-system optimization. Compute, memory, networking, and software must work together efficiently. NVIDIA’s custom Arm CPU is central to this approach. Moore’s Law slowing is creating the need and opportunity for workload-specific CPU and CPU + GPU optimizations with a system-level view driven by hardware/software codesigns. Specialized hardware and tightly optimized systems are now the primary levers for performance improvement.

Arm provides silicon customization options for companies looking to optimize their systems. To help accelerate deployment, Arm last year introduced CSS, pre-integrated and validated solutions that some hyperscalers, like Microsoft, are leveraging to pursue the same optimization advantages with a faster time-to-market.

By leveraging the standard CPU designs from Arm’s Neoverse product range, NVIDIA can concentrate on what matters for accelerated computing: memory bandwidth, interconnect latency, power efficiency, and deep CPU-GPU integration. AI data center performance is about more than raw compute—it requires optimizing the full stack. Grace is built for this, maximizing performance while staying within power limits, lowering inference costs, speeding up model iteration, and reducing training time.

For example, a collaboration with the University of Tokyo’s Earthquake Research Institute provides a compelling example of NVIDIA’s system-level optimization. This project demonstrates how the tight integration of the Grace CPU and Hopper GPU, connected by NVLink-C2C, enables a dramatically faster approach to seismic simulation. Researchers achieved a significant breakthrough by overlapping CPU and GPU computation, which is difficult to achieve with traditional, loosely-coupled architectures. This was accomplished by leveraging the Grace CPU’s large memory capacity to store a history of prior simulation results. These results then fed a data-driven predictor, significantly reducing the number of iterations needed for the GPU’s computationally intensive calculations. This “data-driven method” relies critically on the high-bandwidth, low-latency connection between the CPU and GPU, facilitating efficient communication and shared memory access. The result was a 9x speedup compared to a GPU-only approach, with a corresponding 7x reduction in energy consumption. This exemplifies the shift from optimizing individual chips to optimizing the entire system; it is not merely about combining a fast CPU and a fast GPU, but about co-designing them to unlock new computational paradigms and achieve levels of performance and efficiency unattainable with discrete components.

The Maturity and Momentum of the Arm Software Ecosystem

Podcast: How Arm & NVIDIA Are Shaping the Future of AI and Data Centers

A key factor driving NVIDIA’s decision to adopt Arm for its custom CPU development is the growing maturity and momentum of the Arm software ecosystem. Historically, alternative CPU architectures have struggled to displace incumbents due to software fragmentation and compatibility issues, but modern Arm-based systems no longer face these barriers. Today, software designed for cloud, AI, and enterprise workloads increasingly runs natively on Arm, and new development often targets Arm first.

This transformation is largely driven by a standards-based approach. Initiatives such as Arm SystemReady ensure broad software compatibility at the OS and driver layer, allowing hardware vendors to innovate on microarchitecture, interconnects, and system design while maintaining software portability. This approach allows Arm vendors to build CPUs optimized for acceleration-heavy workloads while running the same software as existing platforms. Applications, drivers, and system software all function seamlessly across Arm-based systems, eliminating the software fragmentation that plagued past alternative architectures.

Beyond application compatibility, the depth of the Arm ecosystem extends to infrastructure software. Low-level drivers, operating systems, and firmware now support Arm natively. This deep integration means that IT teams deploying Arm servers no longer face additional complexity compared to x86, making Arm a viable and scalable choice for data centers.

The increasing presence of Arm-native enterprise software further strengthens its ecosystem. Many workloads, including open-source databases and llama.cpp, are already optimized for Arm. Open-source software has played a crucial role in accelerating Arm adoption, as these projects provide cross-architecture compatibility by default. Independent software vendors (ISVs) like SAP and Oracle are also embracing Arm; many ISVs now maintain Arm-native builds in-house, even if they have yet to formally market them. This growing ISV adoption reinforces that Arm is not an afterthought but a first-class enterprise platform.

The long-standing perception that porting software to Arm is difficult is now outdated. While tuning for peak performance always requires effort—regardless of architecture—simply running existing applications on Arm is straightforward and often seamless. Cloud-based Arm instances, such as those from AWS, Microsoft, and Google Cloud, further ease migration by providing Porting software to a new architecture is simple; the challenge lies in performance tuning, which is true for any processor transition.

Perhaps the most important shift is that Arm is no longer just a secondary option—it is a primary target for modern software development. As Arm-based CPUs continue gaining market share in hyper scale cloud environments, developers are increasingly prioritizing Arm optimizations in their roadmaps. Enterprise IT teams must prepare for a multi-architecture world, where x86 is no longer the default choice but rather one of several competing options.

This shift has profound implications for accelerated computing. As AI workloads scale and compute efficiency become paramount, a strong and mature software ecosystem is essential for supporting architectural innovation. NVIDIA’s Grace CPU strategy is a testament to Arm’s readiness for large-scale deployment. Unlike previous attempts to introduce alternative CPU architectures, Arm’s robust software support, ecosystem momentum, and seamless compatibility with modern workloads position it as a credible and competitive foundation for next-generation AI and high-performance computing infrastructure.

Conclusion: Strategic Implications

NVIDIA’s custom CPU development represents more than incremental improvement—it fundamentally reimagines the role of CPUs in accelerated computing environments. By optimizing CPU design specifically for acceleration-first architectures, NVIDIA addresses critical efficiency and performance challenges that limit conventional approaches.

This strategy aligns with a broader industry shift toward specialized computing solutions optimized for specific workload characteristics rather than general-purpose versatility. As AI and accelerated computing continue transforming the data center landscape, the strategic integration of custom CPUs with specialized accelerators will likely become increasingly important for competitive differentiation.

For data center operators, technology strategists, and enterprise decision-makers, this development highlights the importance of considering integrated system architecture rather than individual component performance when evaluating next-generation computing platforms.

*This white paper was commissioned by Arm. The insights and analyses provided are based on research and data obtained through collaboration with Arm, their partners, and third-party developers. The goal of this paper is to present an unbiased examination of Arm’s technical position in the industry and growth prospects in the data center. While Arm has provided support for this research, the findings and conclusions drawn in this document are those of Creative Strategies, Inc and do not necessarily reflect the views of Arm.

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