AMD: Advancing AI Data Centres, Networks and Robots

2026-07-24 · Source: AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

AMD, led by CEO Dr. Lisa Su, unveiled a comprehensive suite of new hardware and software at its Advancing Artificial Intelligence event on July 24, 2026, targeting data centers, telco networks, and robotics. Key announcements include the ROCm.ai developer platform, designed to simplify AI development with claimed performance improvements of up to 3.3x faster inference and 2.4x faster training. The company also introduced the Helios rack-scale solution, an open infrastructure combining EPYC CPUs, Instinct MI455X GPUs, and ROCm software, offering 50% more high-bandwidth memory capacity and up to 30% more tokens per dollar. Additionally, AMD launched the 6th generation EPYC 9006 Series CPUs for agentic AI, Instinct MI400 Series GPUs for frontier AI and HPC, and the Ryzen AI Embedded X100 Series processors for physical AI applications. A new Robotics Partner Network and a partnership with Schneider Electric for the Helios AI Reference Design were also announced, reinforcing AMD's open ecosystem approach.

Key takeaway

For AI Architects and Machine Learning Engineers designing next-generation infrastructure, AMD's new portfolio offers compelling options. You should evaluate the Helios rack-scale solution for foundation model training, given its 50% greater HBM capacity and 30% more tokens per dollar. Additionally, consider integrating ROCm.ai to streamline your AI development workflows, potentially achieving up to 3.3x faster inference. For edge deployments, explore Ryzen AI Embedded X100 Series processors to power real-time physical AI applications.

Key insights

AMD's strategy focuses on an open ecosystem across hardware and software to accelerate AI development and deployment from data centers to edge robotics.

Principles

• Agentic AI drives significant inference demand.
• Open ecosystems reduce integration efforts.
• Workload-optimized compute improves efficiency.

Method

ROCm.ai provides a cohesive workflow for AI development, deployment, and optimization on AMD infrastructure, integrating CLI, embedded expertise in coding assistants, and an agentic framework for inference optimization.

In practice

• Utilize ROCm.ai for faster AI workload deployment.
• Consider Helios for foundation model training.
• Deploy Ryzen AI Embedded for physical AI.

Topics

• AI Data Centers
• Agentic AI
• ROCm.ai
• Helios Platform
• Instinct GPUs
• Ryzen AI Embedded
• Robotics Partner Network

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Architect, Machine Learning Engineer, Robotics Engineer

Related on AIssential

• AMD makes case for integrated Helios AI stack with new GPUs, CPUs — AMD positions its integrated Helios stack as a comprehensive alternative to Nvidia for AI infrastructure, focusing on inference and agentic AI.
• ROCm 7.13: Expanding Hardware, Tools, and Reach — ROCm 7.13 expands AMD GPU hardware support, virtualization, and developer tools for AI workloads.
• Three insights you may have missed from theCUBE’s coverage of RAISE Summit — Agentic inference demands specialized, efficient AI infrastructure, integrating advanced storage, compute, and financial/sovereignty considerations.
• CES 2026: Everything AMD revealed at the show — AMD's CES 2026 strategy focuses on an "AI everywhere" approach, spanning data center to edge with new silicon and platforms.
• AMD unveils Helios AI rack to challenge Nvidia — AMD's Helios AI rack aims to disrupt Nvidia's AI hardware market dominance, driven by increasing demand for agentic AI compute.
• AMD targets system-level AI infrastructure optimization as agentic workloads reshape enterprise compute — Agentic AI demands system-level optimization and heterogeneous architectures for cost-efficient, scalable compute.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.