Understanding AI Large Language Models and Precision Workstations

Harnessing the power of large language models (LLMs) on Precision workstationsis a transformative leap for businesses. These advanced systems enable rapid and precise data processing and analysis, unlocking insights that drive innovation and competitive advantage. Integrating large language models into operations allows companies to automate complex tasks, enhance decision-making and foster a culture of efficiency, propelling them toward digital age success. The benefits are clear: improved productivity, cost savings and the ability to anticipate market trends and customer needs with remarkable precision. This is not just a technological advancement; it's a strategic imperative for thriving in an increasingly data-driven world.

Back to Basics: What is a Large Language Model and How Does it Work?

LLMs are algorithms that have disrupted content creation with their ability to not only understand language, but also produce natural language with shocking sophistication. LLMs are initially trained on massive datasets of information through a process called deep learning, but an LLM can also learn over time when it is given additional data and retrained. The process is comparable to how a human brain learns from reading and experience. How "smart" the model becomes roughly correlates to the type, quality and quantity of data it was trained on and how many parameters the model has. Parameters are similar to synapses in the human brain, but more simply stated: the more parameters, the more complex the brain. That's why as models are increasing in size, they are also increasing in capability.

Making it Real for Your Business

Day-to-day business tasks are already being revolutionized by the LLM's ability to answer questions on virtually any topic and in any language. For instance, many online productivity tools have AI-powered Q&A to provide accurate and near-instant answers to questions generated from the data stored in that platform. While LLMs are an impressively powerful tool, how can you apply the efficiency of a trained LLM to your business' specific data?

A process known as fine-tuning makes it possible to adapt an LLM to respond based on multiple sources of company-specific knowledge. By fine-tuning a pre-trained LLM on your business data, you turn an existing generic language model into a company expert. The data you include is up to you and the particular use case, but the potential increase in productivity is extraordinary. A popular technique called retrieval-augmented generation (RAG) fetches data that is included alongside the question a user asks, often resulting in an accurate answer without the need for the curation, cost and complexity of the fine-tuning process.

Although they are conversational in nature, LLMs can do much more than Q&A. They can be utilized for marketing by creating and translating copy for blogs or generating tailored product descriptions. They can massively streamline administrative tasks by summarizing progress reports, work journals or meeting transcripts, providing action items to the relevant team members and management so nothing is missed. They have become invaluable in software engineering by refactoring code, performing code review and writing new functions in virtually any software language. Developments in a process called Stable Diffusion have even allowed LLMs to translate text into creative art.

The first platforms that come to mind when someone mentions AI are often conversational AI platforms such as OpenAI's ChatGPT, Google's Gemini and Claude 3 from Anthropic, which are powered by very powerful LLMs hosted in the cloud. These services already have millions of users interacting with them through their respective websites and apps. They can also be accessed programmatically via APIs, allowing software or products to request answers from the conversational AI platform through specialized background requests, seamlessly bringing the power of AI into any aspect of your company's product or workflow.

Cloud-Based LLMs May Have Their Challenges

Although LLMs deployed on the cloud are powerful and easily accessed without any special hardware other than an internet connection, there are several business situations in which a cloud-deployed LLM may not be favorable and an LLM running locally might make sense. For instance:

• Applications that will not have a stable internet connection, such as energy sector, maritime, aerospace, mining or agriculture.

• Any application that will be trained on or receive data that is not suitable to transfer to a third party due to data residency requirements, non-disclosure agreements, intellectual property concerns or local laws.

• Latency-sensitive applications such as a real-time assistant or AI concierge.

• Any application where internet connection or latency could create an issue of safety, such as self-driving cars.

• Applications where paying a small fee for every transaction will add up to an unpredictable or exorbitant cost, such as an AI concierge, which could potentially be asked an unlimited number of questions by its users.

"Cloud-based LLMs charge per token for training, fine-tuning, and running inference, and costs accumulate perpetually over time. Although on-premises solutions have up-front hardware costs, these costs are far more predictable over the lifetime of the service."

If your desired AI application falls into any situations similar to the above, there are indeed several LLMs that can be used on-premises and tuned using deskside workstations, most notably:

• Gemma 7B, a lightweight version of Google's Gemini, is best for lightweight, efficient applications on devices with limited computational resources.

• Llama 3, which excels in high-performance and versatility, is suitable for research and large enterprises.

• The Mistral series is fully open source and offers flexibility and customization for cost-effective deployments.

Most of the above models allow you to choose from several pre-trained models with a varying number of parameters. A model with more parameters will generally perform better on a wider range of tasks but will also generally require more computational resources (i.e., a model with more parameters will require a workstation with more GPU VRAM, memory and processing power). The NVIDIA accelerated computing platform with NVIDIA AdaLovelacearchitecture, which includes NVIDIA RTX GPUs for training LLMs, offers performance increases across the board over previous architectures, with better energy efficiency, lower costs and improved scalability when working with multiple GPUs in tandem.

Dell Precision workstations, such as the Precision 5860 Tower, Precision 7875 Towerand Precision 7960 Tower,are configurable with single, dual or up to quad NVIDIA RTX Ada Generation GPUs and have ample single- or dual-processor configurations available and memory configurations available up to 4 TB (systems vary on configuration options).

With these desktop workstations, you will have the power you need to fine-tune LLMs using the model of your choice while maintaining privacy, data residency and predictable costs.

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