HPE 与 Nvidia 如何用 AI 工厂实现规模化 AI 落地
Operationalize AI At Scale With HPE And Nvidia
HPE AI Factory with Nvidia 是一套预集成基础设施方案，把 Nvidia 的加速计算与软件同 HPE 的安全基础设施、软件、控制平面和服务结合，覆盖从数据摄取、训练、微调到高吞吐 AI 推理的完整生命周期。该方案以 token 吞吐量衡量产出，并针对工作负载专用架构、智能体 AI 和主权 AI 等企业规模化部署挑战提供支持。
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SPONSORED POST
SPONSORED POST: While many organizations have AI projects underway at various stages of maturity , most are experiencing challenges scaling AI across their IT landscape. That is because many AI strategists have yet to work out how to build, operate, and extract value from their AI infrastructure.
Without a formal, top-level plan for AI, key capabilities and requirements get lost, and the full value of AI is never realized.
What is needed is a pre-integrated, full-stack environment that simplifies the deployment, scaling and governance of enterprise AI.
This is where the "AI factory" concept is gaining ground. An AI factory is a specialized computing environment designed to manage the entire AI lifecycle, from data ingestion and training through to fine-tuning and high-volume AI inference. Its primary "product" is intelligence, measured in token throughput, which in turn drives innovation, decision-making, IT automation and future AI applications.
HPE AI Factory with Nvidia is a pre-integrated infrastructure solution with HPE services provided from development to deployment to ongoing support for AI initiatives. It combines Nvidia's accelerated computing and software with HPE's secure infrastructure, software, control plane, and services, to scale as the organization’s needs grow.
The question that follows is how the AI factory model maps onto the main challenges enterprises face when they try to run AI at-scale.
In this Hot Seat interview, James Hayes puts that question to Thierry Pienaar, HPE fellow and Chief Technology Officer for HPC & AI worldwide at HPE, and Kaushik Shirhatti, VP, AI factory at Nvidia. He asks them how the AI factory model is reshaping enterprises thinking about AI and how it is accelerating customer projects.
Pienaar and Shirhatti explain that AI no longer sits in a deployment vacuum, and that today's enterprise-wide rollouts carry risks older architectures were never built to absorb. Secure operations that scale across segments become a core requirement, particularly as deployments take in a widening range of personas, workgroups and, increasingly, agentic inputs.
Security that load-balances intelligently, so that demand does not overwhelm the system, matters just as much. Sovereign AI is another priority: the balance between innovation and control now shapes how customers meet their growing sovereign AI mandates, which have become central to AI governance.
You will learn:
• How AI factories can enable workload-specific architectures.
• How agentic AI is being used within the AI factory context.
• How HPE AI Factory with Nvidia meets the range of challenges customers face, and how it stands apart in its market segment.
• New joint innovations the HPE and Nvidia partnership is bringing to customers.
Learn more about how sovereign AI beats compliance challenges and feeds local innovation with HPE and Nvidia here.
SPONSORED FEATURE: HPE and NVIDIA help organizations apply accelerated computing, software, enterprise infrastructure, networking, control plane, and services to the workloads and business outcomes that matter most.
October 1, 2026
September 30, 2026
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