Editor's note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible and showcases new hardware, software, tools and accelerations for NVIDIA RTX PC and workstation users.
In the rapidly evolving world of artificial intelligence, generative AI is captivating imaginations and transforming industries. Behind the scenes, an unsung hero is making it all possible: microservices architecture.
The Building Blocks of Modern AI Applications Microservices have emerged as a powerful architecture, fundamentally changing how people design, build and deploy software.
A microservices architecture breaks down an application into a collection of loosely coupled, independently deployable services. Each service is responsible for a specific capability and communicates with other services through well-defined application programming interfaces, or APIs. This modular approach stands in stark contrast to traditional all-in-one architectures, in which all functionality is bundled into a single, tightly integrated application.
By decoupling services, teams can work on different components simultaneously, accelerating development processes and allowing updates to be rolled out independently without affecting the entire application. Developers can focus on building and improving specific services, leading to better code quality and faster problem resolution. Such specialization allows developers to become experts in their particular domain.
Services can be scaled independently based on demand, optimizing resource utilization and improving overall system performance. In addition, different services can use different technologies, allowing developers to choose the best tools for each specific task.
A Perfect Match: Microservices and Generative AI The microservices architecture is particularly well-suited for developing generative AI applications due to its scalability, enhanced modularity and flexibility.
AI models, especially large language models, require significant computational resources. Microservices allow for efficient scaling of these resource-intensive components without affecting the entire system.
Generative AI applications often involve multiple steps, such as data preprocessing, model inference and post-processing. Microservices enable each step to be developed, optimized and scaled independently. Plus, as AI models and techniques evolve rapidly, a microservices architecture allows for easier integration of new models as well as the replacement of existing ones without disrupting the entire application.
NVIDIA NIM: Simplifying Generative AI Deployment As the demand for AI-powered applications grows, developers face challenges in efficiently deploying and managing AI models.
NVIDIA NIM inference microservices provide models as optimized containers to deploy in the cloud, data centers, workstations, desktops and laptops. Each NIM container includes the pretrained AI models and all the necessary runtime components, making it simple to integrate AI capabilities into applications.
NIM offers a game-changing approach for application developers looking to incorporate AI functionality by providing simplified integration, production-readiness and flexibility. Developers can focus on building their applications without worrying about the complexities of data preparation, model training or customization, as NIM inference microservices are optimized for performance, come with runtime optimizations and support industry-standard APIs.
AI at Your Fingertips: NVIDIA NIM on Workstations and PCs Building enterprise generative AI applications comes with many challenges. While cloud-hosted model APIs can help developers get started, issues related to data privacy, security, model response latency, accuracy, API costs and scaling often hinder the path to production.
Workstations with NIM provide developers with secure access to a broad range of models and performance-optimized inference microservices.
By avoiding the latency, cost and compliance concerns associated with cloud-hosted APIs as well as the complexities of model deployment, developers can focus on application development. This accelerates the delivery of production-ready generative AI applications - enabling seamless, automatic scale out with performance optimization in data centers and the cloud.
The recently announced general availability of the Meta Llama 3 8B model as a NIM, which can run locally on RTX systems, brings state-of-the-art language model capabilities to individual developers, enabling local testing and experimentation without the need for cloud resources. With NIM running locally, developers can create sophisticated retrieval-augmented generation (RAG) projects right on their workstations.
Local RAG refers to implementing RAG systems entirely on local hardware, without relying on cloud-based services or external APIs.
Developers can use the Llama 3 8B NIM on workstations with one or more NVIDIA RTX 6000 Ada Generation GPUs or on NVIDIA RTX systems to build end-to-end RAG systems entirely on local hardware. This setup allows developers to tap the full power of Llama 3 8B, ensuring high performance and low latency.
By running the entire RAG pipeline locally, developers can maintain complete control over their data, ensuring privacy and security. This approach is particularly helpful for developers building applications that require real-time responses and high accuracy, such as customer-support chatbots, personalized content-generation tools and interactive virtual assistants.
Hybrid RAG combines local and cloud-based resources to optimize performance and flexibility in AI applications. With NVIDIA AI Workbench, developers can get started with the hybrid-RAG Workbench Project - an example application that can be used to run vector databases and embedding models locally whil
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