Deploy llm-d for Distributed LLM Inference on DigitalOcean Kubernetes (DOKS)

Introduction

Large language models (LLMs) are powering a new generation of AI applications, but running them efficiently at scale requires robust, distributed infrastructure. DigitalOcean Kubernetes (DOKS) provides a flexible, cloud-native platform for deploying and managing these workloads.

In this tutorial, you’ll learn how to deploy llm-d—a distributed LLM inference framework—on DigitalOcean Kubernetes using automated deployment scripts. Whether you’re a DevOps engineer, ML engineer, or platform architect, this tutorial will help you establish a scalable, production-ready LLM inference service on Kubernetes.

Estimated Deployment Time: 15-20 minutes

This tutorial focuses on basic llm-d deployment on DigitalOcean Kubernetes with automated scripts.

Key Takeaways

• llm-d is an advanced, open-source distributed LLM (Large Language Model) inference framework purpose-built for Kubernetes environments. It enables scalable, production-grade AI inference by separating prefill (context processing) and decode (token generation) stages, optimizing GPU utilization, and supporting multi-node, multi-GPU deployments. Its disaggregated serving architecture and intelligent resource management allow for efficient, cost-effective, and high-throughput LLM serving—ideal for real-time generative AI applications and large-scale inference workloads.
• DigitalOcean Kubernetes (DOKS) offers a fully managed, cloud-native Kubernetes platform that simplifies the deployment, scaling, and management of containerized AI/ML workloads. With built-in support for GPU nodes (including NVIDIA RTX 4000 Ada, RTX 6000 Ada, and L40S), DOKS provides the infrastructure foundation required for high-performance distributed LLM inference.
• This tutorial provides a step-by-step guide to deploying llm-d on DigitalOcean Kubernetes using automated deployment scripts. You’ll learn how to provision GPU-enabled clusters, configure the NVIDIA device plugin, deploy llm-d components, and validate distributed LLM inference—all with best practices for reliability, scalability, and future extensibility.
• By following this tutorial, you’ll be able to quickly launch a production-ready, scalable LLM inference service on Kubernetes, leverage GPU acceleration, and integrate with your own AI applications using an OpenAI-compatible API endpoint.

Prerequisites

• DigitalOcean account with GPU quota enabled.
• doctl CLI installed and authenticated.
• kubectl installed.
• helm installed.