How to Run Open-Source LLMs on a Cloud Server with a GPU

LLM, both commercial hosted offerings and open-source LLM, have been making impressive strides during 2023.

Commercial LLMs include but are not limited to;

• OpenAI
• DeepMind
• Google
• Anthropic

Regarding cloud providers offering GPUs for running large language models (LLMs) and AI applications, industry leaders include AWS, Azure, and Google Cloud Platform.

Cost-effective cloud servers with powerful enough GPUs include Lambda, CoreWeave, and Runpod. All of those providers offer high-end GPUs, such as the NVIDIA HGX-H100.

Hugging Face’s leaderboard keeps track of the top open-source LLMs you can explore. Here are some of the best options:

• Falcon-40B
• 30B-Lazrus

To maximize the performance and achieve optimal results while using an LLM, it is important to pick the appropriate GPU type on the cloud server, considering the specific GPU requirements of LLM.

RunPod is a cloud-based platform that makes running large language models (LLMs) on servers with GPUs easy. This can be a great way to improve the performance of your LLMs, especially if you are working on projects that require a lot of computing power.

To run an LLM on a server with a GPU using RunPod, you will need to:

1. Create a RunPod account
2. Create a new workspace
3. Select a GPU-enabled server
4. Install the LLM software on the server
5. Train the LLM on the server

1. Create a RunPod account

To create a RunPod account, visit the RunPod website and click the “Sign Up” button. You will need to provide an email address and password.

2. Create a new workspace

You can create a new workspace once you have created a RunPod account. To do this, click the “Workspaces” tab and the “Create Workspace” button. You must provide a name for your workspace and select a region.

3. Select a GPU-enabled server

When you are creating a new workspace, you will need to select a server that has a GPU. To do this, click the “Hardware” tab and select the “GPU” checkbox.

4. Install the LLM software on the server

Once you have selected a server, you must install the LLM software on the server. To do this, follow the instructions provided with the LLM software.

5. Train the LLM on the server

Once you have installed the LLM software on the server, you can train LLM. To do this, follow the instructions provided with the LLM software.

Benefits of Running an LLM on a Server with a GPU

There are several benefits to running an LLM on a server with a GPU. First, GPUs can provide a significant performance boost for LLMs. This is because GPUs are designed for parallel processing, which is ideal for the type of calculations required for LLMs.

Secondly, GPUs can help to reduce the training time for LLMs. GPUs can process more data in parallel, leading to faster training times and making more accurate predictions.

Useful Links

• https://lambdalabs.com/service/gpu-cloud
• https://docs.runpod.io/docs/template-creation
• https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
• https://developer.nvidia.com/blog/introducing-nvidia-hgx-h100-an-accelerated-server-platform-for-ai-and-high-performance-computing/