Three insights you may have missed from theCUBE’s coverage of RAISE Summit

2026-07-16 · Source: AI – SiliconANGLE · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

Agentic inference is profoundly reshaping AI infrastructure, shifting focus from training scale to expanding context windows, memory-augmented reasoning, and continuous GPU data feeding. This makes storage a critical performance component, driving new design patterns for intelligence delivery. Solidigm's Greg Matson emphasizes storage as a new tier extending system memory. AMD optimizes across CPUs, GPUs, and networking with ROCm software for varied workloads. Tensordyne's Napier inference chip achieves power efficiency using a Pareto logarithmic number system, consuming 30 kilowatts for a 72-chip pod versus 150 kilowatts for a comparable Nvidia system. d-Matrix Corsair accelerators are deployed with Nvidia Hopper and Blackwell GPUs for heterogeneous inference, prioritizing low-latency token generation. Hyperscalers are adopting high-capacity SSDs near accelerators to maximize GPU utilization. Capital financing, like Argentum AI's demand-first model, and data sovereignty, including Neo4j's knowledge graphs for deterministic control, are also integral to the AI infrastructure stack.

Key takeaway

For AI Architects and Directors of AI/ML building or optimizing infrastructure for agentic systems, you must move beyond a sole focus on raw compute. Your strategy should integrate high-capacity, high-performance storage as an active memory extension to keep GPUs continuously fed. Evaluate specialized accelerators like d-Matrix Corsair for latency-sensitive tasks and consider power-efficient designs such as Tensordyne's Napier chip. Additionally, proactively address capital financing models and embed data sovereignty principles, potentially using knowledge graphs, into your architectural decisions to ensure control and explainability.

Key insights

Agentic inference demands specialized, efficient AI infrastructure, integrating advanced storage, compute, and financial/sovereignty considerations.

Principles

• Storage functions as an active extension of AI memory.
• Heterogeneous compute architectures optimize varied AI workloads.
• Data sovereignty is an integral AI infrastructure concern.

Method

Tensordyne's Napier chip replaces multiplications with additions using the Pareto logarithmic number system for power-efficient inference. Argentum AI secures customers before capital commitment to finance AI projects.

In practice

• Deploy high-capacity SSDs adjacent to GPUs.
• Combine specialized accelerators for prefill and token generation.
• Integrate knowledge graphs for deterministic AI governance.

Topics

• Agentic AI
• AI Infrastructure
• Storage Systems
• Heterogeneous Computing
• Data Sovereignty
• Knowledge Graphs

Best for: MLOps Engineer, CTO, VP of Engineering/Data, AI Architect, AI Hardware Engineer, Director of AI/ML

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• AMD: Advancing AI Data Centres, Networks and Robots — AMD's strategy focuses on an open ecosystem across hardware and software to accelerate AI development and deployment from data centers to edge robotics.
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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.