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| NVIDIA Grace | |
|---|---|
| Name | NVIDIA Grace |
| Developer | NVIDIA Corporation |
| Release | 2023 |
| Architecture | ARM architecture-based CPU, Heterogeneous computing |
| Cores | varies by model |
| Process | 5 nm |
| Memory | LPDDR5x / HBM (depending on variant) |
| Power | up to 500 W (superchip configurations) |
NVIDIA Grace is a family of high-performance data center processors combining an ARM architecture-based central processing unit with designs optimized for large-memory, low-latency workloads in artificial intelligence and high-performance computing. Announced by NVIDIA Corporation in 2021 and first deployed in production systems in 2023, the product line targets hyperscale inference, training acceleration, scientific simulation, and cloud services. It integrates with accelerator ecosystems and aims to displace traditional x86 servers in specific HPC and AI verticals.
Grace processors were introduced as a response to rising demands from organizations such as OpenAI, Google, Meta Platforms, Microsoft, and national laboratories including Lawrence Livermore National Laboratory and Argonne National Laboratory for large-memory, energy-efficient compute. The roadmap emphasized collaboration with partners like Arm Ltd., Supermicro, HPE, Dell Technologies, and hyperscalers including Amazon Web Services and Google Cloud Platform. Design goals included memory bandwidth comparable to specialized accelerators, interconnects optimized for GPU pairing, and software compatibility with AI stacks from PyTorch, TensorFlow, and vendor libraries like cuDNN.
The architecture centers on an ARM Neoverse-derived CPU microarchitecture implemented on advanced process nodes developed by TSMC. Grace uses a chiplet and interposer strategy in some variants, employing high-bandwidth memory interfaces such as LPDDR5X and integrating coherency and cache hierarchies designed for tight GPU coupling. Interconnect technologies include proprietary high-speed NVLink variants and standards influenced by Compute Express Link and technologies pioneered with Mellanox Technologies acquisition. Security and telemetry draw on features from ARM TrustZone and platform management ecosystems used by vendors like Red Hat and Canonical.
Published benchmarks targeted scientific HPC codes like LAMMPS, GROMACS, and Quantum ESPRESSO, as well as ML workloads exemplified by BERT, GPT-3, and image models evaluated on datasets associated with ImageNet. Grace configurations demonstrated favorable performance-per-watt against x86-based servers from Intel and AMD in memory-bound workloads, with reported gains in throughput for large batch inference compared to multi-socket Xeon or EPYC deployments. Independent evaluations by national labs and cloud providers used standardized suites such as SPEC CPU and AI benchmark collections developed by MLPerf to quantify latency, throughput, and scalability.
The software stack emphasizes support for mainstream AI frameworks including PyTorch, TensorFlow, JAX, and libraries like cuBLAS (where GPU pairing is used). Operating system support includes distributions maintained by Red Hat, Ubuntu (operating system), and cloud images from Amazon Web Services. Developer toolchains leverage compilers from Arm Ltd. and ecosystems like LLVM and GCC, while performance analysis uses profilers from NVIDIA Nsight and benchmarking suites such as MLPerf. Ecosystem partnerships extend to software vendors like Ansys and Schrödinger for computational engineering and chemistry workloads.
The initial product introduced scalar CPU dies and a multi-die "Superchip" variant pairing a CPU die with an accelerator or additional memory die; later variants expanded core counts and memory types to address diverse workloads. OEMs such as Supermicro, HPE, and Dell Technologies offered rack and blade configurations, while cloud providers created instance families on Amazon EC2 and Google Compute Engine. Specialized offerings tailored to edge and embedded HPC were positioned alongside full-scale data center parts used by organizations like NASA and fusion research facilities including Princeton Plasma Physics Laboratory.
Target use cases include large-language-model training and inference for projects similar to ChatGPT-class systems, genomics workloads undertaken by institutions like Broad Institute, climate modeling performed by agencies such as NOAA, and molecular dynamics used by Argonne National Laboratory. Other applications span real-time analytics in finance (adopted by firms like Goldman Sachs and Morgan Stanley), media rendering workflows employed by studios in Industrial Light & Magic, and robotics research groups at MIT and Stanford University.
Deployment models include on-premises clusters at national laboratories and enterprise data centers from providers like Microsoft Azure and Amazon Web Services, as well as managed services from system integrators such as Cray (now part of HPE). Development workflows integrate source-control and CI/CD platforms from GitHub and GitLab, container orchestration with Kubernetes, and model lifecycle tools from companies such as Weights & Biases. Procurement and total-cost-of-ownership assessments consider partnerships with resellers like Arrow Electronics and leasing arrangements common in enterprise IT procurement with firms like Dell Financial Services.