6 Reasons Private LLMs Are Key for Enterprises
With the release of OpenAI’s ChatGPT to the public, large language models (or LLMs) have taken the world by storm, and rightfully so. LLMs are interesting, powerful and offer a new approach to the way we work and interact with computers. For decades we have been interacting with computers using structured methods such as computer programming languages and user interfaces. These structured ways have a high bar of entry, requiring the user to know how to interact with the computer in the way and language that it expects. Large language models flip the script and allow users to interact with a computer in plain language.
Nvidia defines an LLM as “a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other forms of content based on knowledge gained from massive datasets.” Unfortunately, training a large language model is a compute-intensive process requiring hundreds, or even thousands, of graphics cards, terabytes of data and lots of time, which puts the training of a custom model out of reach for all but the largest enterprises. Furthermore, it puts the following questions into consideration:
- What if you need more up-to-date data in the LLM you’re working with?
- What if you need customer-specific data in your LLM?
- What if you need sensitive or private data in the model?
If these concerns resonate with your business, then you need a private LLM.
What Is a Private LLM?
A private LLM can be summarized by a few key tenets:
- It is hosted inside of your compute infrastructure alongside other business workloads.
- It is trained on company, industry or product data. The data available to it is in real time and actionable.
- It provides contextual and accurate information only to the parties that are authorized to access it.
Private LLMs take two main forms. The first comprises custom-trained LLMs using company or industry-specific datasets, while the second form consists of privately hosted LLMs (such as Llama 2.0) coupled with retrieval augmented generation (or RAG). This article focuses on the second form, RAG.
Retrieval Augmented Generation
“RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs’ generative process,” according to IBM.
The use of the word “ground” when describing what RAG does for large language models is an effective way to describe what happens when you provide additional information to the LLM in your query. When an LLM is provided with contextual information in the query, it tends to weigh it more heavily than its larger corpus of information that it was trained on, grounding the model’s response inside that “conversation” in the context it was provided. This method of grounding reduces occurrences of hallucinations, while simultaneously providing your users with more accurate responses.
A typical RAG design is shown below:
There are a great number of benefits of running a private LLM for your company or product, but it all boils down to being able to provide real-time data, in context, to your users, queryable in plain language.
Private LLMs can be used with sensitive data — such as hospital patient records or financial data — and then use the power of generative AI to produce groundbreaking achievements in these fields. With the LLM running on your private infrastructure and only exposed to the people who should have access to it, you can build powerful customer-focused applications, chatbots or just provide an easier way for your employees to interact with your company data — without the risk of sending the data to a third party.
With private LLMs, you can tailor the model and response to your company, industry or customers’ needs. Such specific information is not likely to be included in general or public LLMs. You can feed your LLM with customer support cases, internal knowledge-base articles, sales data, application usage data and so much more, ensuring that the responses you receive are what you’re looking for.
Public LLMs often have to wait months for updates. However, with private LLMs, you can control factors such as update cycles for your users’ needs.
Controlling versioning or the model you’re using is extremely important because if you change the model that you use to create embeddings, you will need to re-create (or version) all the embeddings you store. Versioning your embeddings will allow you to continue using old embeddings since you can continue to reference the old model if necessary.
Reduced Financial Costs
By using a private LLM, you can reduce the cost of purchasing LLMs or proprietary AI software from external companies. This is particularly important for small and medium-sized enterprises (SMEs) and developers with limited budgets, according to LeewayHertz. Additionally, using private LLMs can help companies avoid vendor lock-in, which can become expensive over time.
LLMs trained on more specific information can provide more accurate, specific information. Additionally, it reduces concerns about hallucinations. If you’re still reading this article, there’s a great chance that you’ve used ChatGPT or a similar LLM in the past and experienced odd behavior. Sometimes the LLM will provide you with extremely accurate information and other times it will respond with completely false information, representing it as truth. This behavior is in large part due to the huge, generic datasets on which public LLMs are trained. When you provide very specific context to the LLM, the chances of it responding with a more accurate response increase exponentially.
The performance of public LLMs can sometimes be unreliable. It is not uncommon for their infrastructure to be overloaded, adding additional latency to your query times. As we all know, user attention is finite and adding latency to an interaction only increases the chance of users moving on from your product. Running a private LLM allows you to keep a close eye on the response times of the LLM and increase the resources if and when necessary.
With SingleStore, you can couple relational data with vectors, enabling you to contextualize your queries with real-time data from your applications with 10 to 100 millisecond response times. SingleStoreDB is a distributed, real-time, analytical and transactional database with the power to ensure that your private LLM responds faster than anyone else’s.
As AI proliferates, businesses will demand access to fresh data in real time to provide the right context for foundational models. LLMs and other multistructured foundational models will need to respond to requests in real time and, in turn, will need their data planes to have real-time capabilities to process and analyze data in diverse formats.
To execute on real-time AI, enterprises must continuously vectorize data streams as they are ingested and use them for AI applications. I believe this is key to ensuring that your business is ready for the future that’s already at our doorstep.
If you’re interested in learning all you can about private LLMs, join me at SingleStore Now on Oct. 17 for a hands-on session on how developers can build and scale compelling enterprise-ready generative AI applications. To learn more and to register, visit singlestore.com/now.