NVIDIA and Supermicro: The Essential Gen AI Tech Stack for Achieving Success

Presented by Supermicro and NVIDIA

Unlocking ROI with Generative AI: Strategies for Success

Generative AI presents significant ROI potential, estimated at $2.6 trillion to $4.4 trillion annually across industries. However, it demands substantial computational resources and infrastructure. Join NVIDIA and Supermicro experts as they reveal how to pinpoint essential use cases and establish an AI-ready platform for success.

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Incorporating generative AI into business operations isn’t just beneficial; it’s resource-intensive, requiring more compute, networking, and storage than previous technologies. Efficiently accessing data, customizing pre-trained models, and running them at scale necessitates a comprehensive AI-ready hardware and software ecosystem, along with specialized technical expertise.

Insights from Industry Experts

Anthony Larijani, Senior Product Marketing Manager at NVIDIA, and Yusuke Kondo, Senior Product Marketing Manager at Supermicro, discuss strategies for leveraging generative AI through a conversation moderated by Luis Ceze, Co-founder and CEO of OctoML. They explore key infrastructure decisions, workload considerations, and optimizing AI strategies for your organization.

Infrastructure and Workload Alignment

Aligning infrastructure with organizational needs is paramount. According to Larijani, the first step is to envision your end goals. “Understand what workloads the infrastructure will support. For large-scale foundational models versus real-time applications, the computational requirements differ significantly.”

As you assess workloads, consider scalability. Estimate potential application demand, whether for batch processing or real-time interactions, such as chatbots.

Cloud vs. On-Premises Solutions

Generative AI applications often necessitate scaling, prompting debate over cloud versus on-premises solutions. Kondo emphasizes that it depends on specific use cases and required scale. Cloud offers greater flexibility for scaling; however, on-premises solutions require foresight and significant initial investments.

"Evaluate the potential scale of your project. Is it more cost-effective to use GPU cloud versus building your own infrastructure?” he asks, noting that cloud costs are decreasing while compute power increases.

Open Source vs. Proprietary Models

There’s a growing trend towards customized, specialized models within enterprises. Larijani highlights that techniques like retrieval-augmented generation are enabling businesses to leverage proprietary data efficiently, which impacts infrastructure choices. Tailoring models reduces training costs and times.

“To fine-tune foundational models based on your specific needs enhances both cost efficiency and GPU utilization,” Kondo adds.

Maximizing Hardware with a Comprehensive Software Stack

Optimizing hardware also involves a sophisticated software stack. Kondo states, “Large-scale infrastructure is complex, requiring collaboration with NVIDIA experts right from the design phase to ensure compatibility.”

Building a complete AI software stack is resource-intensive, which is why NVIDIA has transformed into a full-stack computing company. The Nemo framework, part of the NVIDIA AI Enterprise platform, helps businesses build, customize, and deploy generative AI models optimally across extensive infrastructures.

Future-Proofing Against LLM Complexity

As large language models (LLMs) grow, so do their energy needs. Kondo notes, "The expected power for GPUs is increasing rapidly,” prompting innovations in cooling solutions to optimize energy efficiency. Additionally, Larijani points to emerging software development techniques that bolster deployment efficiency while remaining cost-effective and sustainable.

“There’s a rising demand for optimized systems no matter the business size, and new use cases for AI are emerging frequently,” he says, reinforcing the need for ongoing software refinement.

For insights on maximizing generative AI investments and building a robust tech stack for success, don’t miss this enlightening VB Spotlight event.

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Agenda

- Identify enterprise use cases and requirements for success

- Leverage existing models and internal data for tailored solutions

- Utilize accelerated computing for enhanced results and swift decision-making

- Optimize infrastructure for cost-effective speed and performance

- Select appropriate hardware and software solutions for your workloads

Presenters

- Yusuke Kondo, Senior Product Marketing Manager, Supermicro

- Anthony Larijani, Senior Product Marketing Manager, NVIDIA

- Luis Ceze, Co-founder & CEO, OctoML; Professor, University of Washington (Moderator)