maas-base/docs/tutorials/running-deepseek-r1-671b-with-distributed-vllm.md

Running DeepSeek R1 671B with Distributed vLLM

This tutorial guides you through the process of configuring and running the original DeepSeek R1 671B using Distributed vLLM on a GPUStack cluster. Due to the extremely large size of the model, distributed inference across multiple workers is usually required.

MaaS-Base enables easy setup and orchestration of distributed inference using vLLM, making it possible to run massive models like DeepSeek R1 with minimal manual configuration.

Prerequisites

Before you begin, make sure the following requirements are met:

• You have access to a sufficient number of Linux nodes, each equipped with the required GPUs. For example:

GPU	Number of Nodes
H100/H800:8	2
A100/A800-80GB:8	4
A100/A800:8	8

• High-speed interconnects such as NVLink or InfiniBand are recommended for optimal performance.
• Model files should be downloaded to the same path on each node. While MaaS-Base supports on-the-fly model downloading, pre-downloading is recommended as it can be time consuming depending on the network speed.

!!! note

- In this tutorial, we assume a setup of 4 nodes, each equipped with 8 A800-80GB GPUs and connected via 200G InfiniBand.
- A100/A800 GPUs do not support the FP8 precision originally used by DeepSeek R1. Hence, we use the BF16 version from [Unsloth](https://huggingface.co/unsloth/DeepSeek-R1-BF16).

Step 1: Install GPUStack Server

According to the Installation, you can use the following command to start the MaaS-Base server:

sudo docker run -d --name gpustack \
    --restart unless-stopped \
    -p 80:80 \
    --volume gpustack-data:/var/lib/gpustack \
    --volume /path/to/your/model:/path/to/your/model \
    gpustack/gpustack

!!! note

- Replace `/path/to/your/model` with the actual path.

After MaaS-Base server is up and running, run the following commands to get the initial admin password:

sudo docker exec gpustack \
    cat /var/lib/gpustack/initial_admin_password

Step 2: Access MaaS-Base UI

Login to the MaaS-Base UI using the admin user and the obtained password.

http://your_gpustack_server_ip_or_hostname

Step 3: Install MaaS-Base Workers

Navigate to the Workers page in the MaaS-Base UI, click Add Worker button to get the command for adding workers.

And then on each worker node, run the worker adding command to start a MaaS-Base worker:

sudo docker run -d --name gpustack \
    --restart unless-stopped \
    --privileged \
    --network host \
    --volume /var/run/docker.sock:/var/run/docker.sock \
    --volume gpustack-data:/var/lib/gpustack \
    --volume /path/to/your/model:/path/to/your/model \
    --runtime nvidia \
    gpustack/gpustack \
    --server-url http://your_gpustack_server_ip_or_hostname \
	--token your_gpustack_cluster_token

!!! note

- Replace the placeholder paths, IP address/hostname, and cluster token accordingly.
- Replace `/path/to/your/model` with the actual path on your system where the DeepSeek R1 model files are stored.

After all workers are added, return to the MaaS-Base UI.

Navigate to the Workers page to verify that all workers are in the Ready state and their GPUs are listed.

Step 4: Deploy the DeepSeek R1 Model

1. Go to the Deployments page.
2. Click Deploy Model.
3. Select Local Path as your source.
4. Enter a name (e.g., DeepSeek-R1) in the Name field.
5. Specify the Model Path as the directory that contains the DeepSeek R1 model files on each worker node.
6. Ensure the Backend is set to vLLM.
7. After passing the compatibility check, click Save to deploy.

Step 5: Monitor Deployment

You can monitor the deployment status on the Deployments page. Hover over distributed across workers to view GPU and worker usage. Click View Logs to see real-time logs showing model loading progress. It may take a few minutes to load the model.

After the model is running, navigate to the Workers page to check GPU utilization. By default, vLLM uses 90% of GPU memory. You may adjust this in the model configuration settings.

Step 6: Run Inference via Playground

Once the model is deployed and running, you can test it using the MaaS-Base Playground.

1. Navigate to the Playground -> Chat.
2. If only one model is deployed, it will be selected by default. Otherwise, use the dropdown menu to choose DeepSeek-R1.
3. Enter prompts and interact with the model.

You can also use the Compare tab to test concurrent inference scenarios.

You have now successfully deployed and run DeepSeek R1 671B using Distributed vLLM on a MaaS-Base cluster. Explore the model’s performance and capabilities in your own applications.

For further assistance, feel free to reach out to the MaaS-Base community or support team.