# Grace Hopper GH200 GPUs

Oscar has two Grace Hopper GH200 GPU nodes. Each node combines [Nvidia Grace Arm CPU](https://www.nvidia.com/en-us/data-center/grace-cpu/) and [Hopper GPU architecture](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/).

## Hardware Specifications

Each GH200 node has 72 Arm cores with 550G memory. Both CPU and GPU threads on GH200 nodes can now [concurrently and transparently access both CPU and GPU memory](https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper).

## Access

The two GH200 nodes are in the `gracehopper` partition.

### gk-condo Account

A gk-condo user can submit jobs to the GH200 nodes with their gk-gh200-gcondo account, i.e.,

```bash
#SBATCH --account=gk-gh200-gcondo
#SBATCH --partition=gracehopper
```

### CCV Account

For users who are not a gk-condo user, a *High End GPU priority account* is required for accessing the `gracehopper` partition and GH200 nodes. All users with access to the GH200 nodes need to submit jobs to the nodes with the ccv-gh200-gcondo account, i.e.

```bash
#SBATCH --account=ccv-gh200-gcondo
#SBATCH --partition=gracehopper
```

## Running NGC Containers

NGC containers provide the best performance from the GH200 nodes. [Running tensorflow containers ](/oscar/gpu-computing/installing-frameworks-pytorch-tensorflow-jax/installing-tensorflow.md)is an example for running NGC containers.

{% hint style="info" %}
A NGC container must be built on a GH200 node for the container to run on GH200 nodes
{% endhint %}

## Running Modules

The two nodes have Arm CPUs. So Oscar modules **do not** run on the two GH200 nodes. Please contact <support@ccv.brown.edu> about installing and running modules on GH200 nodes.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.ccv.brown.edu/oscar/gpu-computing/gpus/grace-hopper-gh200-gpus.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.