Deploy and Customize AMD Solution Blueprints

2026-04-02 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

AMD Solution Blueprints are ready-to-deploy, customizable reference applications built with AMD Inference Microservices (AIMs), packaged as Helm charts for deployment on an AMD Enterprise AI Suite cluster. This guide demonstrates deploying and customizing these blueprints, including reusing an AIM Large Language Model (LLM) across multiple applications to conserve GPU resources. It covers deploying blueprints like "AutoGen Studio" and "Agentic Translation" from the terminal, swapping the default Llama 3.3 70B Instruct LLM for an alternative like Qwen3-32B, and adjusting hardware configurations such as CPU and ephemeral storage. The process involves using `helm template` and `kubectl apply` commands, with customization achieved via `--set` flags or dedicated override YAML files, validated on a cluster with AMD Instinct MI300X GPUs.

Key takeaway

For AI Engineers and MLOps Engineers deploying AI workloads on AMD hardware, leveraging AMD Solution Blueprints with Helm charts offers a streamlined path to production. You should prioritize reusing existing AIM LLM deployments to optimize GPU resource utilization and consider using YAML override files for managing complex customizations and ensuring version control. This approach simplifies the deployment of multi-agent AI applications and allows for flexible model and resource configuration.

Key insights

AMD Solution Blueprints enable efficient, customizable deployment of AI microservices on AMD Enterprise AI Suite clusters.

Principles

• Reuse AIM LLM services to conserve GPU resources.
• Customize deployments via Helm chart overrides.
• Track configurations using YAML override files.

Method

Deploy Solution Blueprints using `helm template` piped to `kubectl apply`. Customize configurations by passing `--set` flags or a YAML override file to `helm template` to adjust LLM images, precision, and hardware resources.

In practice

• Deploy AutoGen Studio for multi-agent AI conversations.
• Implement Agentic Translation for collaborative text translation.
• Swap LLMs (e.g., Llama 3.3 70B Instruct to Qwen3-32B).

Topics

• AMD Solution Blueprints
• AMD Inference Microservices
• Helm Charts
• Kubernetes Deployment
• LLM Customization

Code references

• amd-enterprise-ai/solution-blueprints
• amd-enterprise-ai/aim-build

Best for: AI Engineer, MLOps Engineer, AI Architect

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