Deloitte and NVIDIA Alliance

The paper discusses the concept of Sovereign Large Language Models (LLMs), a new vision for the development and utilization of Generative AI (GenAI) and LLMs that are specifically tailored to cater to individual nations or regions. The idea is anchored in the belief that local opportunities can flourish where Large Multinational Cloud Provider platforms fall short. These platforms often display bias towards their country of origin in terms of language, culture, and legal frameworks, which can affect the accuracy and reliability of their outputs.

The healthcare and life sciences industries are undergoing dramatic change, as leading organizations adopt artificial intelligence (AI) and other emerging technologies to understand disease and improve treatments and patient care. AI will completely transform healthcare and life sciences as we know it. New and better treatments, personalized medicine, disease prediction, and more—we are at the threshold of a revolutionary period. Federated learning with an enabling platform is one avenue to unleash this promise.

As government agencies integrate AI into their mission and operations, they are likely to encounter challenges due to the constantly evolving demands and contours of AI innovation. An understanding of accelerated computing infrastructure and a detailed AI strategy can help government take the right first step in AI while providing them the flexibility to grow and adapt over time.

Discover how AI enabled through GPUs accelerate the ability to bring efficiency into development teams for AI implementation. Going beyond CPU driven data ML unlocks tools and capabilities in both solutions and infrastructure that create more insightful models, scalable architecture and higher level of customer engagement and efficiencies.

Organizations are striving to realize the strategic goals of improving customer engagement, enhancing operational effectiveness, and finding costs savings. Using AI to meet these targets presents computational demands that have traditionally required an HPC cluster. Today, cloud technologies have made HPC capabilities widely available, with the capacity to manage a “virtual HPC” elastically and as needed. This is a powerful AI accelerator in as much as there is a low barrier to entry.

Knowing what the missing pieces are is only part of the challenge. The next question becomes, where do these technologies fit into the current data architecture and what does that mean for operations and business functions? With our ecosystem of technology partners, we can help you identify the right hardware and infrastructure that aligns with business strategy and goals, and we then work with you to implement the right tools to prepare your data infrastructure for a future with real-time AI and HPC.

Our vertical specialization with cross-solution application in AI and HPC architecture ensures HPC integrates with your platform and embeds AI as a transformational shift in devising and using market leading solutions. We take a technology agnostic stance to help you identify and implement the tools that precisely fit your business challenge. Drawing on our expertise in technology infrastructure and cost optimization, we help ensure the enterprise has the optimal solution and architecture.

The energy consumption and emissions associated with AI training and testing is a more complex calculation than it may at first appear. To bring more clarity to this complicated area, it is helpful to compare AI training energy consumption relative to the underlying microprocessors, namely, CPUs and GPUs. Importantly, not all servers are equal when it comes to AI training, and the time to compute variable has a significant impact on energy usage.

Develop a business strategy that prioritizes business issues the enterprise aspires to solve.

Use accelerated AI platform technology and build capabilities around it in ways that are impossible without access to GPU-enabled computing infrastructure and the skills needed to use it.

Uncover patterns in your data and reveal opportunities to enhance existing models or build new solutions.

Ask and answer the right questions, such as what infrastructure your organization needs, where and when you should use it, and how to anticipate and plan for issues (e.g., latency, security risk) that could lead to costly, unexpected outcomes.

Cost-effectively develop proof of concepts that prove the value of a novel solution.

Leverage our deep industry expertise, ecosystem partnerships, and AI infrastructure capabilities to create deep learning models that can scale sustainably.

Budget, plan, and architect for the future as we advise you in your journey to becoming an AI-fueled organization.