Solution Blueprints: Accelerating AI Deployment with AMD Enterprise AI

2026-02-11 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

AMD Enterprise AI Suite introduces Solution Blueprints, reference implementations designed to accelerate AI workload deployment on ROCm-powered AMD Instinct™ GPUs using Kubernetes. These blueprints are Helm charts that package application layers with AMD Inference Microservices (AIMs), providing validated patterns for production-level AI. They integrate with AMD Enterprise AI Suite cluster services and deploy from an OCI registry via a single Helm command, eliminating manual setup for GPU drivers or ROCm versions. The modular architecture uses reusable Helm "application charts" for shared infrastructure, allowing solution charts to focus on domain logic. The catalog includes seven blueprints for use cases like AutoGen Studio, Continue.dev coding assistant, LLM chat, Financial Stock Intelligence (FSI), Agentic Translation, Talk to Your Documents (RAG), and Agentic Testing, all built with various tools to showcase AIMs' capabilities.

Key takeaway

For MLOps Engineers deploying AI applications on AMD Instinct GPUs, Solution Blueprints offer a standardized, modular approach to accelerate development. You should leverage these pre-integrated Helm charts to quickly set up complex AI workloads like RAG pipelines or agentic systems, reducing configuration overhead and ensuring consistent deployments across your enterprise AI Suite clusters. This allows you to focus on application logic rather than infrastructure orchestration.

Key insights

AMD Solution Blueprints streamline AI deployment on Instinct GPUs via modular Helm charts and pre-integrated AIMs.

Principles

• Standardize inference with microservices.
• Use Helm charts for modular AI application deployment.
• Centralize maintenance for consistent updates.

Method

Deploy AI applications as OCI-compliant Helm charts on Kubernetes, leveraging `helm template` piped to `kubectl apply -f -` for resource creation, and optionally specify existing AIM deployments.

In practice

• Deploy AutoGen Studio for multi-agent workflows.
• Implement RAG pipelines with ChromaDB and Infinity embedding.
• Automate software testing using agentic QA blueprints.

Topics

• AMD Solution Blueprints
• AI Deployment
• Kubernetes Orchestration
• Large Language Models
• Retrieval-Augmented Generation

Code references

• amd-enterprise-ai/solution-blueprints

Best for: Machine Learning Engineer, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.