Building LLM-Powered Enterprise Applications with NVIDIA NIM
With the rapid expansion of language models over the past 18 months, hundreds of variants are now available. These include large language models (LLMs), small language models (SLMs), and domain-specific models—many of which are freely accessible for commercial use. For LLMs in particular, the process of fine-tuning with custom datasets has also become increasingly affordable and straightforward.
Building LLM-Powered Enterprise Applications with NVIDIA NIM
As AI models become less expensive and more accessible, an increasing number of real-world processes and products emerge as potential applications. Consider any process that involves unstructured data—support tickets, medical records, incident reports, screenplays, and much more.
The data involved is often sensitive, and the outcomes are critical to the business. While LLMs make hacking quick demos deceptively easy, establishing the proper processes and infrastructure for developing and deploying LLM-powered applications is not trivial. All the usual enterprise concerns still apply, including how to:
- Access data, deploy, and operate the system safely and securely.
- Set up rapid, productive development processes across the organization.
- Measure and facilitate continuous improvement as the field keeps developing rapidly.
Deploying LLMs in Enterprise Environments
Deploying LLMs in enterprise environments requires a secure and well-structured approach to machine learning (ML) infrastructure, development, and deployment. This post explains how NVIDIA NIM microservices and the Outerbounds platform together enable efficient, secure management of LLMs and systems built around them.
Stage 1: Developing Systems Backed by LLMs
The first stage in building LLM-powered systems focuses on setting up a productive development environment for rapid iteration and experimentation. NVIDIA NIM microservices play a key role by providing optimized LLMs that can be deployed in secure, private environments. This stage involves fine-tuning models, building workflows, and testing with real-world data while ensuring data control and maximizing LLM performance. The goal is to establish a solid development pipeline that supports isolated environments and seamless LLM integration.
Stage 2: Collaboration and Continuous Improvement
In this stage, the focus shifts to collaboration and continuous improvement. By implementing automated pipelines, organizations can continuously improve and update their LLM models while maintaining stability. This stage emphasizes the importance of gradual deployments, monitoring, and version control to manage the complexities of LLM systems in live environments.
Stage 3: CI/CD and Production Roll-Outs
In this final stage, the focus shifts to integrating continuous integration and continuous delivery practices to ensure smooth, reliable production roll-outs of LLM-powered systems. By implementing automated pipelines, organizations can continuously improve and update their LLM models while maintaining stability. This stage emphasizes the importance of gradual deployments, monitoring, and version control to manage the complexities of LLM systems in live environments.
Continuous Delivery with CI/CD Systems
Following DevOps best practices, LLM-powered systems should be deployed through a CI/CD pipeline, such as GitHub Actions. This setup enables continuous deployment of system improvements, which is crucial for systems undergoing rapid iterations, a common scenario with LLMs.
Isolating Business Logic and Models, Unifying Compute
To enable stable, highly-available production deployments, they must be securely isolated from development environments. Under no circumstances should development interfere with production (and vice versa).
Integrating LLM-Powered Systems into their Surroundings
The LLM-powered systems on Outerbounds are not isolated islands. They are connected to upstream data sources, such as data warehouses, and downstream systems consuming their results. This poses additional challenges to deployments, as they have to behave well in the context of other systems too.
Start Building LLM-Powered Production Systems with NVIDIA NIM and Outerbounds
In many ways, systems powered by LLMs should be approached like any other large software system that is subject to stochastic inputs and outputs. The presence of LLMs is similar to a built-in chaos monkey which, when approached correctly, forces building more resilient systems by design.
LLMs are a new kind of a software dependency that is particularly fast-evolving and must be managed as such. NVIDIA NIM delivers LLMs as standard container images, which enables building stable and secure production systems by leveraging battle-hardened best practices, without sacrificing the speed of innovation.
Get started with NVIDIA NIM and Outerbounds.
Conclusion
In this article, we have explored the challenges and opportunities of building LLM-powered enterprise applications with NVIDIA NIM and Outerbounds. We have discussed the importance of establishing proper processes and infrastructure for developing and deploying LLM-powered applications, as well as the need for secure and well-structured approaches to machine learning (ML) infrastructure, development, and deployment.
FAQs
Q: What are the benefits of using NVIDIA NIM and Outerbounds for building LLM-powered enterprise applications?
A: NVIDIA NIM and Outerbounds provide a secure and well-structured approach to machine learning (ML) infrastructure, development, and deployment, enabling the building of stable and secure production systems by leveraging battle-hardened best practices.
Q: What are the challenges of building LLM-powered enterprise applications?
A: The challenges of building LLM-powered enterprise applications include establishing proper processes and infrastructure for developing and deploying LLM-powered applications, as well as the need for secure and well-structured approaches to machine learning (ML) infrastructure, development, and deployment.
Q: How can I get started with NVIDIA NIM and Outerbounds?
A: You can get started with NVIDIA NIM and Outerbounds by setting up a productive development environment for rapid iteration and experimentation, and by implementing automated pipelines for continuous improvement and deployment.
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