Understanding and Enabling the Transformational Power of LLMs

Robert Daigle, Director, Global AI Business at Lenovo

Recently, large language models (LLMs) have taken the world by storm, particularly highlighted by the launch of ChatGPT. This revolutionary advancement has spawned a multitude of opportunities and produced an almost equal number of quandaries.

As generative AI becomes a strategic imperative for unlocking proprietary business value around the globe, Lenovo is committed to being the most trusted partner and empowering customers’ intelligent transformation by simplifying AI deployment and removing complexities, recently unveiling an additional US$1 billion investment in accelerating one-stop enablement for critical AI capabilities, like LLMs and other critical generative AI workloads, through its expanded network of best-in-class AI Innovators and more than 150 turnkey AI solutions. As the company propels its AI technology and momentum forward, the investment also focuses on empowering LLMs for users, enabling AI-driven efficiencies and insights that are set to transform businesses, as well as entire industries.

Lenovo further embraces and advances AI with end-to-end solutions for a wide range of industry verticals, intelligent devices to meet the needs of generative AI, and operational advances to manufacturing, supply chain operations, and more.

Beyond ChatGPT

ChatGPT, as a cutting-edge chatbot, has showcased unparalleled capabilities, from crafting intricate papers to coding programs, and even successfully passing the Bar Exam. Its rapid adoption and widespread usage have led to it becoming the fastest-growing application, surpassing all others on the internet.

While ChatGPT has introduced us to the astounding capabilities of LLMs, a critical question arises: how can we harness the potential of LLMs to drive value for our organizations responsibly? This query is of paramount importance as the transformative potential of LLMs can shape industries and redefine workflows. The versatility of generative AI, of which LLMs are a remarkable example, holds the key to automating tasks, making lives more enjoyable, and infusing work with greater meaning.

The heart of this transformation lies in the concept of LLMs, which stand at the forefront of technological innovation. These models, with their adeptness at natural language processing and producing human-like text, unlock the doors to more efficient communication and automation. The transformative power of LLMs becomes even more pronounced when considering their application across various domains, from content creation to customer interaction. Let’s unpack what it is, and how you can get started.

One of the most important foundations of generative AI is that it should be deployed in a secure and responsible manner. Deploying LLMs and generative AI applications in your own data center or on your edge infrastructure provides data sovereignty and greater control over your AI systems. Organizations should also consider establishing a responsible AI methodology to ensure they’re avoiding potential pitfalls on their AI journey.

What are the differences between Generative AI, Large Language Models and Foundation Models?

Generative AI, LLMs, and foundation models are closely related concepts in the field of artificial intelligence, each with distinct characteristics that contribute to their unique roles in various applications.

Generative AI is a broad term encompassing any machine learning model capable of dynamically creating output after training. This capacity to generate complex output, such as text, images, or code, distinguishes generative AI from other machine learning approaches. It aims to replicate patterns and create new instances based on learned data distributions. Generative AI models can produce content autonomously, making them versatile tools for content creation, data augmentation, and creative tasks.

Large Language Models (LLMs) are a specific category of generative AI models specialized in handling language-related tasks. These models are trained on vast text datasets and can generate human-like text responses, perform language translation, answer questions, and more. They excel in understanding context and generating coherent text based on the input provided. Examples include OpenAI’s GPT series and BERT.

Foundation models are pretrained LLMs that serve as the base architecture for generative AI applications. They provide a framework with pretrained components that can be fine-tuned for specific tasks. These models capture general language understanding and can be adapted for various applications with additional data. They play a vital role in the development of custom generative AI applications.

What are some of the top use-cases?

LLMs start with various inputs: audio, video, text, code, etc, which can all be enhanced and converted to other forms like chatbots, translations, code, avatars, etc.

These use cases cross most verticals — healthcare, manufacturing, finance, retails, telecommunications, energy, government, technology — and drive significant business impact.

Life science industry use of GenAI and LLMs is expanding and offering great promise for foundational research. These models are being used to generate images of biological structures and processes facilitating enhanced understanding and moving us closer to precision medicine.

The financial services industry is already adopting generative AI models for certain financial tasks. These institutions are using generative AI to enhance efficiency, improve customer experiences and reduce operational costs.

In manufacturing, generative AI enables industries to design new parts that are optimized to meet specific goals and constraints like performance and precision.

For operations, GenAI models can help optimize supply chains, improve demand forecasting, provide better supplier risk assessments, and improve inventory management. Generative AI can analyze large amounts of historical sales data, incorporating factors such as seasonality, promotions, and economic conditions.

Across all these industries, generative AI can also help create synthetic data for development and training.

The blueprint for deploying LLMs

Generative AI and LLMs are extremely computationally intensive. Significant improvements in workload performance and usage cost for compute resources can be gained by using optimized software, libraries, and frameworks that leverage accelerators, parallelized operators and maximize core usage. As described above, generative AI LLMs require accelerated computing resources. In an extended partnership with VMware to help customers harness the value of their data, Lenovo’s Reference Design for Generative AI carefully considers every detail to optimize performance.

Leveraging the NVIDIA AI Enterprise software platform, which includes the NVIDIA NeMo framework, Lenovo’s newest Reference Design for Generative AI based on Large Language Models (LLMs) shows businesses how to deploy and commercialize powerful generative AI tools and foundation models, using a pre-validated, fully integrated and performance-optimized solution for data centers. Coupled with the recently announced VMware Private AI, businesses can achieve great performance in their model with vSphere and VMware Cloud Foundation GPU integrations. The solution features the most GPU-dense platforms that are purpose-built for AI workloads, including the Lenovo ThinkSystem SR675 V3 and ThinkSystem SR670 V2, which are the most versatile, accelerated computing platforms on the market with three server configurations in one, including support for NVIDIA HGX A100 4-GPU systems with NVLink and Lenovo Neptune hybrid liquid cooling, as well as 4- or 8-GPU configurations featuring NVIDIA L40S and NVIDIA H100 Tensor Core GPUs or NVIDIA H100 NVL servers in a compact 3U footprint.

To support critical network integration, Lenovo is adding to its the latest NVIDIA Spectrum-X networking technology from NVIDIA to its AI portfolio. Using NVIDIA BlueField data processing units (DPUs) and NVIDIA Spectrum-4 switches in its generative AI reference design, Lenovo is providing the option for the most advanced way to integrate AI workloads in enterprise data centers today.

Responsibility and Ethics

Responsible AI is a governance framework that covers ethical, legal, safety, privacy, and accountability concerns. Although the implementation of responsible AI varies by company, the necessity of it is clear. Without responsible AI practices in place, a company is exposed to serious financial, reputational, and legal risks. On the positive side, responsible AI practices are becoming prerequisites to even bidding on certain contracts, especially when governments are involved; a well-executed strategy will greatly help in winning those bids. Additionally, embracing responsible AI can contribute to a reputational gain to the company overall.

The new Lenovo AI Discover Center of Excellence provides access to Lenovo data scientists, AI architects and engineers to help explore, deploy and scale AI solutions. The service also guides customers to the most appropriate software partners, AI-optimized infrastructure and responsible AI guidance through the Lenovo Responsible AI Committee. As companies learn to deploy AI, the committee helps customers with their approach to designing, deploying, and using AI ethically, helping organizations understand and address privacy, fair usage, diversity, equity, inclusion, and accessibility considerations.

Together, these advancements serve to simplify the rollout of AI, making LLMs accessible to organizations of all sizes and enable transformative intelligence across all industries.


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