> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GH200

> Specifications, pricing, and availability for the GH200 GPU instance

Powered by a single NVIDIA GH200 Grace Hopper Superchip, this instance offers a unique architecture that combines a 72-core Grace CPU with a Hopper GPU. Its defining feature is the 576 GB unified memory pool (96 GB HBM3e + 480 GB LPDDR5X) connected via the high-speed NVLink-C2C interconnect. This design eliminates traditional PCIe bottlenecks and allows the GPU to access a massive memory space, making it unparalleled for running extremely large models on a single machine.

## Specifications

| Feature                   | Detail                                                                                                     |
| :------------------------ | :--------------------------------------------------------------------------------------------------------- |
| **Category**              | Professional AI & Graphics                                                                                 |
| **Instance ID**           | `gd-1xgh200`                                                                                               |
| **GPU**                   | 1x NVIDIA GH200 Grace Hopper                                                                               |
| **GPU RAM**               | 96 GB                                                                                                      |
| **GPU Connectivity**      | PCIe                                                                                                       |
| **CPU Model**             | NVIDIA Grace Arm v9 (3.10 GHz)                                                                             |
| **vCPUs**                 | 72                                                                                                         |
| **RAM**                   | 480 GB                                                                                                     |
| **Local Storage**         | 7.68 TB                                                                                                    |
| **Network Speed**         | Dual-port 100GbE                                                                                           |
| **Default GPU driver**    | `595`                                                                                                      |
| **Compatible GPU driver** | `580`, `595`                                                                                               |
| **Availability**          | [**RNO2A**](/platform/regions/us-west#rno2a)<br />[**US-EAST-04A**](/platform/regions/us-east#us-east-04a) |

## Primary use cases

Low-latency inference for models that are too large to fit in the memory of a standard GPU, large-scale graph analytics, and memory-intensive data science.

## Recommended models

Inference on heavily quantized versions of modern state-of-the-art models (500B - 1T parameters) like Kimi K2.6, GLM 5.1, as well as moderately quantized versions of other large models such as DeepSeek V4 Flash and MiniMax-M2.7 in the 100B-500B parameter range.