Posts by Daniel Gustafsson

Deploy and Customize AMD Solution Blueprints

AMD Solution Blueprints are ready-to-deploy, customizable reference applications built with AMD Inference Microservices (AIMs). They offer a microservice solution for a range of use cases, from standard chat interfaces to agentic frameworks, serving as both starting points for development and example implementations.

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Leveraging AMD AI Workbench to Scale LLM Inference for Optimal Resource Utilization

Explore how autoscaling with AMD Inference Microservices (AIMs) and AMD AI Workbench can automatically scale your resources in response to shifting AI workload demand. AI inference can be computationally intensive, with resource requirements that vary depending on traffic e.g., the number of inference requests your workload receives at any given time. Autoscaling addresses this by scaling resources up during peak traffic to maintain performance, and scaling them back down during quieter periods to reduce cost and resource consumption.

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Getting Started with AMD Resource Manager: Efficient Sharing of AMD Instinct™ GPUs for R&D Teams and AI Practitioners

In this blog, you will learn how to use AMD Resource Manager and its components for centralized AI infrastructure governance. It’s part of the AMD Enterprise AI Suite, a full-stack solution for developing, deploying and running AI workloads on a Kubernetes platform designed to support AMD compute. The AMD Resource Manager provides a user-friendly graphical user interface (GUI) and Command Line Interface (CLI) with a unified control plane that simplifies tasks such as managing compute clusters, user access, monitoring resource utilization, and allocating the right compute quotas to the right projects.

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Getting Started with AMD AI Workbench: Deploying and Managing AI Workloads

In this blog, you will learn how to use AMD AI Workbench, an easy to use graphical user interface (GUI) with command-line interface (CLI) support for running and managing AI workloads. It’s part of the AMD Enterprise AI Suite, a full-stack solution for developing, deploying and running AI workloads on a Kubernetes platform designed to support AMD compute. AMD AI Workbench is designed to offer AI developers accelerated end-to-end AI development, from experimentation to production deployment.

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