For a growing number of companies, “AI is becoming the highest priority of innovation,” Meena says. “Personally, I have worked with a lot of financial services, technology, even healthcare customers, where they are really into the full implementation of AI because they see not only a lot of value, but it pushes their specialization ahead.”
How LLM and RAG models fuel early adopters
Meena says, “enterprises are eager to embrace” two interrelated areas of AI, Large Language Models (LLM) and Retrieval Augmented Generation (RAG).
RAG helps users by facilitating informed decision-making by making optimum use of an enterprise’s core knowledge and databases. RAG breaks down large unwieldy datasets into more compact better organized, vectorized data that can be quickly and efficiently optimized and accessed, even in real-time.
“RAG, Retrieval Augmented Generation …is the technique that essentially optimizes the output of a large language model,” Meena explains. “It ..integrates an authoritative knowledge base and or database outside of its originally trained data sources. So, we get the company’s specific data set and use it either as a structured database or unstructured data.”
This new customized access to and use of data is fueling early adopters. Many new applications and use cases, notably ChatGPT, among others are helping transform enterprises in many industries. As Meena noted, “..RAG can help recognize text and speech, make meaning out of written words and respond with domain- specific knowledge from research results to help customers."
AMD EPYC™, Ryzen ™ and other CPUs built around our Zen Core Architecture are essential infrastructure for emerging AI models, applications and use cases. Early AI adopters use LLM and RAG for emerging applications to deepen ties to existing and attract new consumer and corporate customers and to optimize and expand their use of their own internal resources, Meena explains. Learn more about how EPYC supports AI, including RAG, here.
LLM, RAG applications and implementation challenges
Applications for LLM and RAG are useful to many industries. Meena cited just a few examples of how these models are already being used by AMD customers: recommendation systems, personalization of product sales, content creation and management, data analysis and insights and customer service and support. Meena adds that in the face of the enthusiasm for LLM and RAG implementing them is not without its challenges. RAG and LLM models are large and complex requiring significant processing power and AI expertise. Meena and I talked about some of the challenges enterprises face: privacy and security, data/model bias and related ethical and legal issues, quality and accuracy of AI models, scale and integration into existing workflows, training and familiarity with data sets, the use of open source AI models and cost management.
AMD helps enterprises overcome RAG, LLM implementation challenges
AMD is uniquely qualified to help enterprises adopt LLM and RAG solutions to meet their specific needs. AMD can also address the challenges enterprise will face throughout the process. Meena cites AMD’s Zen architecture, AI expertise and end-to-end pipeline.
“I do want to emphasize one thing especially in a RAG- like end-to-end pipeline, we will really take full advantage of our AI portfolio at AMD.”
Our Zen Core Architecture including EPYC and Ryzen CPUs and their high core count, advanced security, energy efficiency, scalability and efficiency are essential infrastructure for these new AI solutions, Meena adds. AMD offers deep AI expertise to each of our customers. This includes our partnerships with cloud suppliers and mega data centers, software stack management, preconfigured software libraries, customization of open source models and new techniques for balancing model efficiency and precision.
Optimizing AI models: balancing efficiency and precision
One of the key challenges is balancing precision and efficiency, Meena explains. AMD works with enterprises to ensure efficiency and precision even while implementing lower precision data structures. “We can’t indefinitely keep growing [RAG model] sizes without [increasing] costs [of] the compute and memory footprint for these models… and the cycles you need to train or deploy these models also grow,” she adds.
Meena talked about one of the more intriguing LLM and RAG developments, ‘low precision’ techniques such as quantization and model distillation that make it possible for AMD enterprise customers to achieve efficiency without compromising accuracy. This helps enterprises balance cost and precision of valuable new LLM and RAG models.
I am excited about these new solutions and how AMD can help your enterprise competitively differentiate itself with LLM and RAG. You can learn more about how AMD is helping early adopters implement and customize these models by listening to our entire discussion here.
