NVIDIA GH200 Grace Hopper™ Superchip | Cloud Servers for AI, ML, & HPC Applications

The most efficient large memory supercomputer

Designed for AI training, inference, and HPC

The NVIDIA GH200 empowers businesses to foster innovation and unearth new value by enhancing large language model training and inference. It further amplifies recommender systems through expanded fast-access memory, and facilitates deeper insights via advanced graph neural network analysis.

The power of coherent memory

The NVIDIA NVLink-C2C interconnect provides 900GB/s of bidirectional bandwidth between CPU and GPU for 7x the performance found in accelerated systems. The connection provides unified cache coherence with a single memory address space that combines system and HBM GPU memory for simplified programmability.

Performance and
speed

GH200 will deliver up to 10x higher performance for applications running terabytes of data, helping scientists and researchers reach unprecedented solutions for the world's most complex problems.

Specifications
Grace CPU Cores	72 Cores
CPU LPDDR5X bandwidth	Up to 500 GB/s
GPU HBM bandwidth	4TB/s HBM3
NVLink-C2C bandwidth	900GB/s total, 450GB/s per direction
CPU LPDDR5X capacity	480GB
GPU HBM capacity	96GB HBM3

Specifications

Grace CPU Cores

72 Cores

CPU LPDDR5X bandwidth

Up to 500 GB/s

GPU HBM bandwidth

4TB/s HBM3

NVLink-C2C bandwidth

900GB/s total, 450GB/s per direction

CPU LPDDR5X capacity

480GB

GPU HBM capacity

96GB HBM3

Additional resources

FAQ

What makes the GH200 different from other GPUs?

Unlike traditional GPUs, the GH200 Superchip integrates an NVIDIA Grace CPU and H100 GPU in a single package connected via NVLink-C2C technology. This architecture significantly reduces latency and increases memory bandwidth, making it ideal for AI training, inference, and complex simulations.

What are the key features of the NVIDIA GH200?

These GPUs excel in:

Integrated Grace CPU + H100 GPU architecture
Up to 600GB/s NVLink-C2C memory bandwidth
High-speed LPDDR5X memory for power efficiency
Optimized for AI, ML, and HPC workloads
Designed for large-scale AI models and cloud computing

What workloads benefit the most from the GH200?

The NVIDIA GH200 is ideal for:

AI training and deep learning (large language models, generative AI)
Scientific simulations and HPC (climate modeling, physics simulations)
Big data processing and analytics
Autonomous vehicle research
Cloud computing and large-scale inference workloads

What memory and storage options are available with the GH200?

The GH200 Superchip supports up to 96GB of HBM3 memory and high-speed LPDDR5X RAM, enabling extreme memory bandwidth. It is optimized for workloads requiring high memory throughput and efficient data movement.

How does the GH200 improve AI model training?

By integrating CPU and GPU resources within a single superchip, the GH200 reduces data movement latency and increases parallel processing efficiency, significantly accelerating AI training and inference tasks.

How do I get started with NVIDIA GH200 Grace Hopper™ Superchip on Vultr?

To get started with the NVIDIA GH200 Grace Hopper™ Superchip on Vultr, log into your Vultr account and navigate to the Cloud GPU section. Select the GH200 option when deploying your instance, configure the server specifications, and complete the deployment process.

How does the NVIDIA GH200 compare to traditional CPU-GPU architectures?

Traditional CPU-GPU architectures rely on PCIe connectivity, which introduces bottlenecks in data transfer. The GH200 Superchip uses NVLink-C2C technology, enabling faster, direct communication between the Grace CPU and H100 GPU for ultra-low latency.

Does the GH200 support multi-GPU scaling?

Yes, the GH200 supports multi-GPU configurations via NVLink, allowing businesses to scale AI and HPC workloads across multiple interconnected GH200 Superchips.

What is the NVIDIA GH200 Grace Hopper Superchip?

The NVIDIA GH200 Grace Hopper Superchip is a high-performance AI accelerator designed for advanced AI, machine learning, high-performance computing (HPC), and cloud workloads. It combines an NVIDIA Grace CPU with an H100 Tensor Core GPU, enabling massive computational power with ultra-fast memory bandwidth. The NVIDIA Grace CPU is an Arm CPU with Arm Neoverse™ V2 cores for advanced performance and energy efficiency.

What are the performance advantages of the NVIDIA GH200’s NVLink-C2C interconnect over PCIe Gen5 in AI and HPC workloads?

TThe GH200’s NVLink-C2C delivers up to 900 GB/s total bandwidth – significantly outperforming PCIe Gen5 – by providing a coherent memory model between the Grace CPU and Hopper GPU. This eliminates traditional bottlenecks associated with CPU-GPU communication and allows larger AI models to operate efficiently across unified memory, improving performance in training, inference, and data-intensive HPC applications.

How does the GH200’s unified memory architecture improve large-scale, large-model deployment?

Combining up to 96GB of HBM3 on the Hopper GPU with LPDDR5X memory on the Grace CPU and linking them with NVLink-C2C, the GH200 enables memory pooling and coherent access across the entire system. This architecture supports larger model contexts without offloading or partitioning, allowing generative AI and LLMs to run more efficiently with reduced overhead and higher throughput.

Can the NVIDIA GH200 accelerate Retrieval-Augmented Generation (RAG) architectures for GenAI applications?

The GH200’s high memory bandwidth and unified CPU-GPU design are ideal for low-latency data retrieval and real-time inference – key requirements for RAG pipelines. The Grace CPU efficiently handles retrieval from vector databases, while the Hopper GPU executes inference with minimal delay, delivering superior performance for production-grade GenAI solutions.