GPU-Accelerated LLM Mistral 7B on EKS | Erik Krieg

The article provides a detailed guide on how to run a GPU-accelerated open-source Large Language Model (LLM) inference workload using Elastic Kubernetes Service (EKS). The author uses Mistral AI’s 7 billion parameter model and serves it with Hugging Face’s text-generation-inference server on an EKS cluster. The article covers the technical aspects of using AWS EKS, Karpenter for provisioning GPU nodes, NVIDIA’s k8s-device-plugin to expose GPUs to pods, and Mistral 7B LLM. It also provides a step-by-step guide on how to expose GPUs to pods, figure out GPU requirements, provision GPU nodes with Karpenter, and deploy the Text Generation Inference server.

The author also provides a bonus section on running a local chat client using Gradio and the Hugging Face InferenceClient. The article concludes with a reminder to scale down pods when not in use to avoid unnecessary costs and provides additional resources for further reading. The guide is intended for those with a technical background and familiarity with the tools and concepts discussed.

Key takeaways:

The article provides a guide on how to run a GPU-accelerated open-source Large Language Model (LLM) inference workload using Elastic Kubernetes Service (EKS).
The AI model used for demonstration is Mistral AI’s 7 billion parameter model and it is served with Hugging Face’s text-generation-inference server on an EKS cluster.
The article also explains how to make GPUs accessible to pods on the cluster, figure out GPU requirements, provision GPU nodes with Karpenter, expose GPUs to pods with NVIDIA K8S Device Plugin, and deploy the Text Generation Inference server.
As a bonus, the article provides a guide on how to run a local chat client using Gradio and Hugging Face's InferenceClient.

GPU-Accelerated LLM Mistral 7B on EKS | Erik Krieg | Prodigy Engineering

Key takeaways:

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