Self-Hosting GPU-Accelerated LLM (Mistral 7B) on Kubernetes (EKS)

Dec 31, 2023 ·

GPU accelerated Machine Learning (ML) workloads have become increasingly popular in recent years. The ability to leverage GPUs to speed up complex ML algorithms and models has helped organizations of all sizes and industries adopt ML technology. In this article, we explore running GPU-accelerated LLM (Large Language Model) workloads on Amazon EKS (Elastic Kubernetes Service).

Amazon EKS is a fully managed Kubernetes service that makes it easy to deploy applications in the cloud. EKS helps customers manage, secure, and scale their applications. It also provides features such as autoscaling, resource quotas, and monitoring for applications running on Kubernetes clusters. Additionally, EKS also supports GPU-accelerated workloads with NVIDIA drivers pre-installed on its nodes.

To setup a GPU-accelerated LLM workload on EKS, first we create a Kubernetes cluster which will contain both CPU and GPU nodes. Next, we use the NVIDIA device plugin to detect the GPUs in each node. This allows us to assign the GPUs to specific pods. We then install the necessary software packages, including CUDA, cuDNN, and NCCL, onto each node. Finally, we deploy our LLM workload onto the cluster.

Once the cluster is setup, we can launch our LLM workloads. This is done by launching a Docker container with the LLM code and mounting the input data into the container. The LLM workloads are then distributed over the GPUs of the cluster using the NVIDIA device plugin. This allows the GPUs to be used simultaneously, resulting in improved performance.

In addition to running GPU-accelerated LLM workloads, Amazon EKS also provides other services to make ML development faster and easier. For example, it provides support for automated hyperparameter tuning, as well as pre-trained ML models that can be used as starting points for developers.

Overall, running GPU-accelerated LLM workloads on Amazon EKS provides organizations with an easy and scalable solution for deploying and managing ML applications. By leveraging GPUs to speed up complex ML algorithms and models, businesses of all sizes can get started quickly and take advantage of ML technology without having to invest in expensive hardware or specialized expertise.