AMD: AIMs 2.2 Brings Inference Microservices for Instinct, EPYC, and Radeon

What Are AMD Intelligence Modules and What Does Version 2.2 Bring?

AMD Intelligence Modules (AIMs) version 2.2 are standardized Docker inference microservices — that is, ready-to-use containerized services for serving AI models via API — with automatic configuration tailored to AMD hardware. Instead of manually tuning drivers, libraries, and parameters for each accelerator separately, the AIMs package automatically detects available hardware and applies optimal settings. Version 2.2 extends support to three classes of AMD hardware: Instinct data-center GPUs, EPYC server processors, and Radeon professional GPUs. Compared to solutions that require separate installation procedures for each accelerator type, AIMs offers a unified deployment pattern.

Which Models Run on Which Hardware?

AMD has defined reference models for each platform. On Instinct GPUs (MI300X, MI325X, MI350X, and MI355X), Gemma-4 31B and Mistral-Small 24B are available — more demanding models that take advantage of the large HBM memory in those accelerators. On the EPYC CPU (model 9965), Llama-3.1 8B, Qwen3 ranging from 4B to 35B parameters, and GPT-OSS 20B are available, positioning EPYC as a capable CPU-only inference platform without a GPU. Radeon professional GPUs (W7900 and R9700 Pro) support Llama-3.1 8B, Qwen3-VL 8B for visual-language tasks, and Gemma-3n E4B, a compact multimodal model.

How Are AIMs 2.2 Deployed in Production?

Deployment is done via Helm charts in Kubernetes environments, meaning AIMs microservices integrate into standard cloud-native pipelines without AMD-specific operational tools. AMD also releases the Document Summarization Blueprint alongside version 2.2 — a reference architecture for production use with a configuration of 188 CPU cores and 128 GB RAM. For comparison, similar NVIDIA Triton Inference Server deployments typically require manual definition of model_repository configuration and separate per-model optimization steps; AIMs consolidates this into a single auto-configuration procedure.

Why Is This Important for AMD’s Position in the AI Ecosystem?

AIMs 2.2 directly addresses one of the biggest barriers to broader AMD hardware adoption for AI inference: the complexity of setting up the ROCm environment compared to the NVIDIA CUDA ecosystem. With standardized Docker packages, AMD reduces friction during the transition, especially for teams already using Kubernetes and Helm in their MLOps processes. Support for three hardware classes — from data-center GPUs to desktop Radeon cards — suggests AMD is building a vertically integrated inference stack that covers both cloud and edge use cases.