> This page is for Evo 2, version 1.0.1.
> For other versions, use one of these documentation indexes:
> - 2.2.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/evo2/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/evo2/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/evo2/2.0.0/llms.txt
> - 1.0.2: https://docs.nvidia.com/nim/bionemo/evo2/1.0.2/llms.txt
> - 1.0.1: https://docs.nvidia.com/nim/bionemo/evo2/1.0.1/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/evo2/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Prerequisites and Support Matrix for Evo 2 NIM

## Models

| Model Name    | Parameters       | Context Length | Publisher     |
|---------------|------------------|----------------|---------------|
| Evo 2         | 40 Billion       | 1 Million      | Arc Institute |

## Hardware Support

Run your Evo 2 NIM on any NVIDIA GPU that meets following minimum hardware requirements:

| GPU    | GPU Memory (GB) | Precision | # of GPUs | Disk Space (GB) | CPU RAM (GB) |
|--------|-----------------|-----------|-----------|-----------------|--------------|
| H100   | 80              | Mixed     | 2         | 100             | 16           |
| H200   | 144             | Mixed     | 1         | 100             | 16           |

The GPU Memory, CPU RAM, and Disk Space values are in GB; Disk Space is for both the container and the model.

If the precision is mixed, it means the model features layers with FP32, BF16, and FP8 floating-point precision tensors.

Supported CPU architectures: x86_64 / amd64.

## Software Prerequisites

* Begin with Linux-based [Docker-supported operating system](https://docs.docker.com/engine/install/#supported-platforms)
* Install [Docker](https://docs.docker.com/engine/install/) - minimum version: 23.0.1
* Install [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/) - minimum version: 535
* Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-the-nvidia-container-toolkit) - minimum version: 1.13.5
* Verify your container runtime supports NVIDIA GPUs by running

```bash
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```

Example output:

```bash
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.78.01    Driver Version: 525.78.01    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  N/A |
| 41%   30C    P8     1W / 260W |   2244MiB / 11264MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
```

<Note>
For more information on enumerating multi-GPU systems, see the NVIDIA Container Toolkit's [GPU Enumeration Docs](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html?highlight=enumerate#gpu-enumeration)
</Note>

## NGC (NVIDIA GPU Cloud) Account

1. [Create an account on NGC](https://catalog.ngc.nvidia.com/)
2. [Generate an API Key](https://org.ngc.nvidia.com/setup/api-key)
3. Log in to Docker with your NGC API (enter the key as password when prompted.)

```bash
docker login nvcr.io --username='$oauthtoken'
```