Dell Quantized AI Models
Since generative artificial intelligence (GenAI) has completely changed the computing landscape, Dell clients are eager to work with large language models (LLMs) to create cutting-edge new capabilities that will boost output, efficiency, and creativity within their businesses. With the widest range of AI infrastructure available globally, encompassing cloud and client devices, Dell Technologies offers comprehensive end-to-end solutions and services tailored to customers’ specific needs, regardless of their AI journey stage.
Additionally, Dell provides hardware solutions designed to accommodate AI workloads, ranging from mobile and stationary workstation PCs to servers for high-performance computing, data storage, networking switches, cloud-native software-defined infrastructure, data security, HCI, and services. However, one of the most common queries from Dell users is how to find out if a PC is compatible with a specific LLM. They will make an effort to assist in responding to your query and offer some advice on configuration decisions that users should think about when utilizing GenAI.
Generative AI with Model Dell Quantized
Start by thinking about some fundamentals of how to manage an LLM on a PC. NVIDIA RTX GPUs presently dominate the market for AI processing in PCs with specialized circuits called Tensor cores, even though AI routines can also be handled by the CPU or a new class of dedicated AI circuitry called an NPU. The core of AI processing, mixed precision mathematical computing, is made possible by the design of RTX Tensor cores.
However, completing the calculations is just one aspect of the story; given their possible memory footprint, LLMs also need to take available memory space into account. The LLM processing needs to fit into the GPU VRAM in order to optimize AI performance on the GPU. Systems can be easily scaled to fit thanks to NVIDIA’s scalable GPU lineup, which offers options for GPU VRAM and Tensor core count across both mobile and fixed workstation offerings. Remember that some fixed workstations have the ability to support multiple GPUs, which further expands their capacities.
Although there are a growing number and variety of LLMs available, the parameter size of the chosen LLM is one of the most crucial factors in determining hardware requirements. Take Llama-2 LLM from Meta AI. There are three distinct parameter sizes available: seven, thirteen, and seventy billion parameters. Higher parameter sizes typically translate into more accurate LLM results and increased suitability for general knowledge applications.
Post-training of Dell quantized
Customers must understand the demands the LLM will place on the machine and how to best manage the model, regardless of whether their objective is to take the foundation model and run it exactly as is for inferencing or to modify it to fit their unique use case and data. The most innovative and profitable applications of AI projects for customers have been found in the development and training of models against specific use cases using customer-specific data. When creating new features and applications using LLMs, the largest parameter size models can have extremely high performance requirements for the machine. For this reason, data scientists have developed methods to manage the accuracy of the LLM output while minimizing processing overhead.
And one of those methods is Dell Quantized. It is a method for shrinking LLMs by altering the internal parameters (weights’) mathematical precision. Bit precision reduction affects the LLM in two ways: it lowers the processing footprint and memory requirements and affects the LLM’s output accuracy. Dell Quantized is comparable to JPEG image compression in that a higher degree of compression can produce images that are more effective, but a lower degree of compression can produce images that are unreadable in certain situations.
Let’s examine an example of how the amount of GPU memory needed can be decreased by Dell Quantized an LLM.
In real terms, this means that customers can choose from a variety of Dell Precision workstations if they wish to run the Llama-2 model Dell Quantized at 4-bit precision.
The requirements increase when operating at higher precision (BF16), but Dell offers solutions that can handle any size LLM and any required level of precision.
Another method known as fine-tuning can increase accuracy given the possible effects on output accuracy. It works by retraining a subset of the LLM’s parameters on your unique data to increase the output accuracy for a particular use case. Fine-tuning can speed up the training process and increase the accuracy of the output by changing the weight of some of the trained parameters. Dell Quantized combined with fine-tuning can produce small language models tailored to a particular application, which can then be deployed to a wider range of devices with even lower AI processing power requirements. Once more, using Precision workstations as a sandbox to build GenAI solutions gives developers the confidence to fine-tune an LLM.
Retrieval-Augmented Generation (RAG) is another technique to control the output quality of LLMs. In contrast to traditional AI training methods, which are static and out-of-date due to the data used during training, this approach offers current information. RAG establishes a dynamic link between pertinent data from reliable, pre-selected knowledge sources and the LLM. By using RAG, users can better understand how the LLM generates the response, and organizations can exert more control over the generated output.
When combined and integrated, these different approaches to working with LLMs are not exclusive and frequently result in increased performance efficiency and accuracy.