Designed for Greater Energy Efficiency with No Performance Compromise
NVIDIA designed the NVIDIA Grace CPU to be a new kind of high-performance, data center CPU—one built to deliver breakthrough energy efficiency and optimized for performance at data center scale. Accelerated computing is enabling giant leaps in performance and energy efficiency compared to traditional CPU computing. To deliver these speedups, full-stack innovation at data center scale is required, spanning chips, systems, software, and algorithms.
The Right Architecture for the Right Workload with the Best Energy-Efficiency Performance
As workloads are increasingly accelerated, there remain use cases that today primarily run on traditional CPUs—particularly code that is sparse and "branchy" serialized tasks such as graph analytics. At the same time, data centers are increasingly power-constrained, limiting the growth of their capabilities. This means that all workloads that can be accelerated should be accelerated. Those that cannot be accelerated must be run on the most efficient compute possible, and the CPU must be optimized for those workloads.
The NVIDIA Grace CPU: Combining High-Performance and Energy-Efficiency
The NVIDIA Grace CPU combines 72 high-performance and energy-efficient Arm Neoverse V2 cores, connected with the NVIDIA Scalable Coherency Fabric (SCF). The NVIDIA SCF is a high-bandwidth, on-chip fabric that provides a total of 3.2 TB/s of bisection bandwidth—double that of traditional CPUs. A high-bandwidth on-chip fabric is needed to deliver maximum system-level performance by maintaining the data flow among CPU cores, cache, memory, and system input and output.
Unparalleled Performance and Energy Efficiency
The NVIDIA Grace CPU is designed to deliver outstanding performance, memory bandwidth, and data-movement capabilities with breakthrough performance per watt. At the data center level, this translates into a generational leap in performance and outstanding total cost of ownership (TCO). The Grace architecture delivers these benefits in a data center-grade, general-purpose CPU, which means it provides versatility and performance across a broad range of foundational data center workloads such as microservices, data analytics, graph analytics, and simulation.
Delivering Consistent Performance
Beyond its exceptional performance and energy efficiency, the Grace CPU is designed to sustain consistent performance levels with deterministic performance. The NVIDIA SCF removes data movement bottlenecks. By combining a high-bandwidth fabric and a wide LPDDR5X memory interface, the Grace CPU achieves over 90% STREAM efficiency (a measure of delivered memory bandwidth relative to peak-rated bandwidth) even when all cores are active. In contrast, competitive systems will reach just over 80% max efficiency, dropping to around 70% when all cores are active.
Conclusion
The NVIDIA Grace CPU is a game-changing innovation in data center computing, offering unparalleled performance and energy efficiency. With its unique architecture, the Grace CPU is poised to revolutionize the way data centers are designed and operated, enabling a new level of performance, efficiency, and sustainability.
Frequently Asked Questions
- What is the NVIDIA Grace CPU?
The NVIDIA Grace CPU is a new kind of high-performance, data center CPU designed to deliver breakthrough energy efficiency and optimized for performance at data center scale. - What are the key features of the NVIDIA Grace CPU?
The key features of the NVIDIA Grace CPU include its high-performance and energy-efficient Arm Neoverse V2 cores, its high-bandwidth on-chip fabric, and its wide LPDDR5X memory interface. - What is the benefit of the NVIDIA Grace CPU?
The benefit of the NVIDIA Grace CPU is that it delivers unparalleled performance and energy efficiency, enabling data centers to achieve a generational leap in performance and outstanding total cost of ownership (TCO). - What workloads is the NVIDIA Grace CPU suitable for?
The NVIDIA Grace CPU is suitable for a broad range of foundational data center workloads, including microservices, data analytics, graph analytics, and simulation.
