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The NVIDIA Grace CPU Superchip is a two-processor server module, not a single desktop CPU: it links two Arm-based Grace CPUs over NVLink-C2C. NVIDIA lists 144 Neoverse V2 cores, memory options up to 960 GB, and up to 128 PCIe Gen 5 lanes. The headline figures describe platform capacity, and the memory bandwidth in particular varies by configuration.
What is the Grace CPU Superchip?
Grace CPU Superchip combines two Grace CPU chips in one module. NVIDIA says they communicate coherently over NVLink-C2C, with up to 900 GB/s of bidirectional bandwidth. NVIDIA introduced the platform for data-center workloads such as AI infrastructure and high-performance computing, rather than as a standard desktop processor upgrade. NVIDIA’s 2022 launch announcement and its 2023 architecture article describe the design.
Grace Superchip specifications
| Specification | What NVIDIA lists |
|---|---|
| CPU cores | 144 Arm Neoverse V2 cores across the two CPUs |
| Memory | 240 GB, 480 GB, or 960 GB LPDDR5X with ECC, according to NVIDIA’s Grace Performance Tuning Guide |
| Memory bandwidth | Up to 1,024 GB/s for 240 GB and 480 GB options; up to 768 GB/s for the 960 GB option, per NVIDIA’s tuning guide |
| Interconnect | NVLink-C2C, up to 900 GB/s bidirectional bandwidth |
| PCIe | Up to 128 PCIe Gen 5 lanes, arranged as eight x16 interfaces, with bifurcation options described by NVIDIA |
| Cache | 228 MB L3 in NVIDIA’s tuning guide; its 2023 architecture article reports 234 MB |
| Power | 500 W TDP including memory, as listed in NVIDIA’s 2023 architecture article |
These figures are not all interchangeable maximums. The current tuning guide’s configuration table specifies less memory bandwidth for the 960 GB option than for the 240 GB and 480 GB options. NVIDIA’s broad architecture summary says “up to 1 TB/s”; that maximum should not be assumed for every memory configuration. The differing L3 figures also come from different NVIDIA documents, so use the tuning guide when choosing among current memory configurations and treat each specification in the context of its source.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →What the 144 cores mean
The 144 cores are Arm Neoverse V2 cores with Armv9-family features, including SVE2 vector capability. A high core count can benefit work that runs effectively in parallel, but it does not by itself establish how fast a particular application will run. Performance depends on the software, its ability to use the cores and vector instructions, memory behavior, and the complete system configuration.
#1 Best Overall
- Experience the raw power of the NVIDIA GB10 Grace Blackwell Superchip. Delivering 1 PFLOPS of FP4 AI performance, this workstation handles 200B+ parameter models locally with sparsity. This is the same architecture powering the world’s most advanced data centers, brought directly to your desk for zero-latency development.
- Pre-installed with NVIDIA DGX OS, the GN100 is tuned for the full NVIDIA AI stack—CUDA, PyTorch, NIM microservices, and the NeMo Framework. The NVIDIA GB10 Grace Blackwell Superchip pairs a 20-core Arm CPU with a Blackwell GPU featuring fifth-generation Tensor Cores, delivering 1 PFLOP of FP4 AI performance with sparsity. Prototype reasoning models locally and deploy to DGX cloud or data centers with zero code changes.
- Eliminate the bottleneck between CPU and GPU. The GN100 unified memory architecture lets the Blackwell GPU and 20-core Arm CPU access a shared 128GB pool of LPDDR5X-8533 memory over NVLink-C2C—coherent, addressable, and bottleneck-free. This architecture enables 200B+ parameter models to run locally on hardware that would choke a standard desktop, providing the capacity and bandwidth required for real-time inference at scale.
- Two 200Gbps ConnectX-7 ports. Direct-attach a second GN100 for 405B-parameter inference. Add a RoCE 200 GbE switch and link up to four units in a high-speed cluster—the standard configuration for university labs and B2B teams scaling distributed training. Combined with 128GB of LPDDR5X coherent unified memory per node, the GN100 scales as your models scale. Quiet luxury, server-class throughput.
- For proprietary models and regulated datasets, every byte stays on-device. The GN100 ships with a 4TB self-encrypting NVMe SSD, an integrated Kensington lock, and a tamper-resistant 1.2kg sealed chassis. Pair with NVIDIA NemoClaw for sandboxed agentic workflows and policy-based privacy controls. Build, fine-tune, and run sensitive workloads without a single packet leaving your lab.
Software compatibility needs checking before deployment. NVIDIA’s tuning guide says Grace implements Armv9.0-A and can execute application binaries built for Armv8 through Armv8.5 targets. That does not make Grace compatible with x86 binaries. The guide also cautions that NVIDIA HPC compiler fixed-length binaries are not necessarily binary-compatible between processors such as Graviton and Grace. Check the application, compiler, libraries, and deployment environment rather than inferring compatibility from the processor family alone.
What 960 GB of memory and its bandwidth are for
The memory is co-packaged LPDDR5X with error-correcting code (ECC). A 960 GB option provides substantial capacity for data sets and workloads that benefit from keeping more working data close to the CPUs. Capacity and bandwidth are separate considerations: NVIDIA lists up to 768 GB/s for the 960 GB option, compared with up to 1,024 GB/s for its 240 GB and 480 GB options in the tuning guide. Select a configuration according to the workload’s capacity and data-throughput requirements, not the largest number in a headline.
Rank #2
- Warranty Disclosure: The original manufacturer’s warranty is void due to hardware upgrade. This product is covered by a 1-Year seller warranty and LIFETIME seller tech support from the date of purchase.
- LOCAL LLM DEVELOPMENT AND INFERENCE: Built for AI developers and machine learning engineers who want to prototype, test and run generative AI locally. The GB10 Grace Blackwell Superchip and 128GB unified memory are designed to support inference with models up to 200 billion parameters and fine-tuning with models up to 70 billion parameters.
- AI AGENTS, RAG AND CODING WORKFLOWS: Create private chatbots, coding assistants, autonomous agents, tool-using applications and retrieval-augmented generation systems. Local processing reduces dependence on cloud APIs and gives developers greater control over models, data, latency and ongoing usage costs.
- PRIVATE ON-PREMISES AI FOR TEAMS: Designed for startups, enterprises and professional creators that need to keep proprietary code, models and sensitive datasets within their own environment. Its compact desktop form factor, 10Gb Ethernet and ConnectX-7 networking make it practical for offices, laboratories and multi-system AI development.
- ROBOTICS, COMPUTER VISION AND EDGE AI: Suitable for developers creating robotics, smart-camera, computer-vision, industrial automation and edge AI applications. Prototype perception pipelines, multimodal models and intelligent systems locally before moving validated workloads to compatible production infrastructure.
NVIDIA’s Grace CPU Superchip datasheet presents the product in settings such as HPC, enterprise computing, analytics, cloud infrastructure, and intelligent edge systems. Those are deployment contexts, not a guarantee that every application benefits equally. The sources describe architecture and NVIDIA benchmarks, not independent results for every workload.
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What 128 PCIe Gen 5 lanes allow
The 128 lanes are system I/O capacity for connecting devices, not extra CPU cores or memory. NVIDIA describes eight PCIe Gen 5 x16 interfaces and bifurcation options. Its technical materials name device categories including GPUs, DPUs, ConnectX SmartNICs, E1.S and M.2 NVMe storage, and management components. Which devices a system can actually support depends on the server or platform design; check the OEM’s configuration and compatibility information.
Rank #3
- Extreme AI Performance: Powered by NVIDIA GB10 Grace Blackwell Superchip delivering 1 petaFLOP of AI performance and 128GB memory for 200B model fine-tuning.
- Developer-Optimized Platform: Designed for AI developers building secure, long-running agentic workflows, with compatibility across frameworks such as OpenClaw and NemoClaw, supporting private on-device inference, sandboxed execution, and governed data access.
- Scalable Architecture: Featuring NVIDIA NVLink-C2C for ultra-fast CPU-GPU memory communication and NVIDIA ConnectX-7 networking to support dual GX10 system stacking, unlocking superior scalability and performance.
- Advanced Thermal Design: Engineered cooling ensures sustained high performance and reliability in an ultra-small form factor.
- Full Stack AI Solution: The GB10 and NVIDIA AI software stack provide a full stack solution for AI development and deployment.
How to evaluate performance claims
NVIDIA’s March 22, 2022 launch announcement reported a lab-estimated SPECrate2017_int_base score of 740 and compared it with a dual-CPU system shipping with DGX A100 at the time, using the same class of compilers. This is a dated NVIDIA estimate, not an independent test or a prediction for every application.
The datasheet also describes NVIDIA benchmark configurations, including a 480 GB Grace Superchip system compared with specified AMD EPYC and Intel Xeon configurations, along with operating-system, compiler, and workload details. Treat those results as vendor tests and inspect the named configurations and workloads before drawing comparisons. For a meaningful purchase or deployment decision, compare the exact workload and benchmark, Arm software support, per-core behavior, memory configuration, power, I/O implementation, system availability, and total cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How it is deployed
Grace CPU Superchip is intended to be integrated into data-center servers and platforms. NVIDIA describes it as comparable in role to the heart of a dual-socket server, brought together in a compact module. For most buyers, the relevant product is a complete Grace-based server or integrated platform—not a generic CPU accessory or drop-in desktop replacement. NVIDIA’s Grace CPU Superchip product page provides its platform positioning.
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- [Personal AI Supercomputer]: Built for AI developers, researchers, data scientists, startup labs, and university labs, the ASUS Ascent GX10 is designed for local AI development, model testing, inferencing, RAG workflows, and agentic AI experimentation beyond a standard mini PC.
- [NVIDIA GB10 Grace Blackwell Superchip]: Powered by the NVIDIA GB10 Grace Blackwell Superchip with Blackwell GPU architecture and a 20-core Arm CPU, GX10 delivers up to 1 PetaFLOP of FP4 AI performance for generative AI prototyping and local model workflows.
- [128GB Unified Memory for Large AI Workloads]: 128GB LPDDR5x unified memory helps support demanding AI development and testing scenarios, including workflows for large language models, multimodal AI, local inference, fine-tuning experiments, and model evaluation.
- [2TB NVMe Storage for AI Projects]: The 2TB M.2 2242 NVMe SSD provides high-speed local storage for AI model libraries, datasets, Docker containers, checkpoints, development environments, and RAG or vector database workflows.
- [DGX OS and Advanced Connectivity]: DGX OS and the NVIDIA AI software stack help streamline CUDA, PyTorch, TensorFlow, TensorRT, NVIDIA NIM, and AI Blueprint workflows, while Wi-Fi 7, 10GbE, USB-C, HDMI, and NVIDIA ConnectX-7 support modern lab and desktop deployments.
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