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NVIDIA DGX GH200

Trillion-Parameter Supercomputing for Emerging AI

[DGX-GH200]

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The World’s first GPU-CPU superchip AI system, the DGX GH200, with 128 per system petaFLOPS FP8 AI performance. The superchip is named after pioneer American computer scientist and programmer, Grace Hopper. Crafted with 80 billion transistors, 2,304 Arm® Neoverse SVE2 4X 128b V2 Cores, 19.5 TB shared memory and 128 petaFLOPS of FP8 AI performance.
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Grace Hopper DGX™ - The Universal Superchip and System for AI

The NVIDIA Grace Hopper Superchip architecture is the first true heterogeneous accelerated platform for high-performance computing (HPC) and AI workloads. It accelerates applications with the strengths of both GPUs and CPUs while providing the simplest and most productive distributed heterogeneous programming model to date. Scientists and engineers can focus on solving the world’s most important problems.

AI models and Deep Neural Networks are rapidly growing in size and complexity, in response to the most pressing challenges in business and research.

The computational capacity needed to support today’s modern AI workloads has outpaced traditional data centre architectures. Modern techniques that exploit use of model parallelism are colliding with the limits of inter-GPU bandwidth, as developers build increasingly large accelerated computing clusters and push the limits of data centre scale. A new approach is needed - one that delivers almost limitless AI computing scale in order to break through the barriers to achieving faster insights.

NVIDIA NVLink 4th Generation

Fourth-generation NVLink enables accessing peer memory using direct loads, stores, and atomic operations, enabling accelerated applications to solve larger problems more easily than ever.

DGX GH200 is the first supercomputer to pair Grace Hopper Superchips with the NVIDIA NVLink Switch System, a new interconnect that enables all GPUs in a DGX GH200 system to work together as one. The previous-generation system only provided for eight GPUs to be combined with NVLink as one GPU without compromising performance. The DGX GH200 architecture provides 48x more NVLink bandwidth than the previous generation, delivering the power of a massive AI supercomputer with the simplicity of programming a single GPU.

NVLink-C2C

NVLink-C2C memory coherency increases developer productivity, performance, and the amount of GPU-accessible memory. CPU and GPU threads can now concurrently and transparently access both CPU and GPU resident memory, allowing developers to focus on algorithms instead of explicit memory management.

Memory coherency enables you to transfer only the data you need, and not migrate entire pages to and from the GPU. It also enables lightweight synchronization primitives across GPU and CPU threads by enabling native atomic operations from both the CPU and GPU. NVLink-C2C with Address Translation Services (ATS) leverages the NVIDIA Hopper Direct Memory Access (DMA) copy engines for accelerating bulk transfers of pageable memory across host and device. With up to 512 GB of LPDDR5X CPU memory per Grace Hopper Superchip, the GPU has direct high-bandwidth access to 4x more memory than what is available with HBM. Combined with the NVIDIA NVLink Switch System, all GPU threads running on up to 256 NVLink-connected GPUs can now access up to 150 TB of memory at high bandwidth.

Data Center Scalability with Nvidia Networking

NVIDIA DGX GH200 is the only AI supercomputer that offers a shared memory space of 19.5TB across 32 Grace Hopper Superchips, providing developers with over 30X more fast-access memory to build massive models.

DGX GH200 is the first supercomputer to pair Grace Hopper Superchips with the NVIDIA NVLink Switch System, which allows 32 GPUs to be united as one data-centre-size GPU. Multiple DGX GH200 systems can be connected using NVIDIA InfiniBand to provide even more computing power. This architecture provides 10X more bandwidth than the previous generation, delivering the power of a massive AI supercomputer with the simplicity of programming a single GPU.

Giant Memory for Giant Models

As the complexity of AI models has increased, the technology to develop and deploy them has become more resource intensive. However, using the NVIDIA Grace Hopper Superchip architecture, DGX GH200 achieves excellent power efficiency.

Each NVIDIA Grace Hopper Superchip is both a CPU and GPU in one unit, connected with superfast NVIDIA NVLink-C2C. The Grace™ CPU uses LPDDR5X memory, which consumes one-eighth the power of traditional DDR5 system memory while providing 50% more bandwidth than eight-channel DDR5. And being on the same module, the Grace CPU and Hopper™ GPU interconnect consumes 5X less power and provides 7X the bandwidth compared to the latest PCIe technology used in other systems.

Hopper - 5th Generation Tensor Cores & Precisions

First introduced in the NVIDIA Volta architecture, NVIDIA Tensor Core technology has brought dramatic speedups to AI, bringing down training times from weeks to hours and providing massive acceleration to inference.

The NVIDIA Hopper architecture builds upon these innovations by bringing new precisions -Tensor Float (TF32) and Floating Point 64 (FP64) - to accelerate and simplify AI adoption and extend the power of Tensor Cores to HPC.

TF32 works just like FP32 while delivering speedups of up to 20X for AI without requiring any code change. Using NVIDIA Automatic Mixed Precision, researchers can gain an additional 2X performance with automatic mixed precision and FP16 adding just a couple of lines of code. And with support for bfloat16, INT8, and INT4, Tensor Cores in NVIDIA H200 Tensor Core GPUs create an incredibly versatile accelerator for both AI training and inference. Bringing the power of Tensor Cores to HPC, H200 also enables matrix operations in full, IEEE-certified, FP64 precision.

Proven Infrastructure Solutions Built with Trusted Data Center Leaders

As an Nvidia Elite Partner, we offer a portfolio of infrastructure solutions that incorporates the Hopper architecture and the best of the NVIDIA DGX POD reference architecture.

Delivered as fully integrated, ready-to-deploy, these solutions make data centre AI deployments simpler and faster for IT.

Part No. DGX-GH200
Manufacturer nvidia
End of Life? No
Performance 128 petaFLOPS of FP8 AI performance
Compatible CPU(s) Nvidia GH200
Max # Core(s) 2,304 Arm® Neoverse V2 Cores with SVE2 4X 128b
No. of GPUs 256
GPU Memory 144TB
Memory Expansion 19.5 TB
Storage Capacity OS: 2X 960GB NVME SSDs Internal Storage: 30TB (8X 3.84TB) NVME SSDs
Supported OS Ubuntu Linux Host OS
DGX-GH200

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