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 today’s rapidly advancing world of artificial intelligence, generative AI is sparking innovation and revolutionizing industries. While the spotlight shines on AI, the unsung hero enabling this transformation is microservices architecture.
The Backbone of Modern AI Applications
Microservices have emerged as a game-changer in software architecture, reshaping how software is designed, developed, and deployed.
A microservices architecture dissects applications into a set of loosely coupled, individually deployable services. Each service handles a specific function and interacts with other services through well-defined APIs. This modular approach differs significantly from traditional, monolithic architectures where all features are bundled into a single, tightly integrated application.
By breaking down services, teams can work on different components simultaneously, speeding up development processes and enabling independent updates without impacting the entire application. Developers can concentrate on enhancing specific services, leading to improved code quality and quicker issue resolution. This specialization allows developers to become experts in their respective areas.
Services can be scaled independently based on demand, optimizing resource utilization and enhancing overall system performance. Furthermore, different services can utilize different technologies, enabling developers to choose the most suitable tools for each task.
The Perfect Fusion: Microservices and Generative AI
The microservices architecture is ideally suited for developing generative AI applications due to its scalability, modularity, and flexibility.
AI models, particularly large language models, demand significant computational resources. Microservices facilitate efficient scaling of these resource-heavy components without impacting the entire system.
Generative AI applications often involve multiple stages such as data preprocessing, model inference, and post-processing. Microservices enable the development, optimization, and scaling of each stage independently. Additionally, as AI models and techniques evolve rapidly, a microservices architecture allows for seamless integration of new models and replacement of existing ones without disrupting the entire application.
NVIDIA NIM: Streamlining Generative AI Deployment
With the rising demand for AI-driven applications, developers encounter challenges in effectively deploying and managing AI models.
NVIDIA NIM inference microservices offer optimized containers containing pretrained AI models for deployment in various environments. Each NIM container includes all necessary runtime components, simplifying the integration of AI capabilities into applications.
NIM presents a revolutionary approach for developers seeking to incorporate AI functionality by offering streamlined integration, production readiness, and flexibility. Developers can focus on application development without grappling with the complexities of data preparation, model training, or customization, as NIM inference microservices are performance-optimized and support industry-standard APIs.
AI Empowerment: NVIDIA NIM on Workstations and PCs
Developing enterprise generative AI applications poses numerous challenges. While cloud-hosted model APIs serve as a starting point for developers, concerns around data privacy, security, model response time, accuracy, API costs, and scalability often impede progress.
Workstations featuring NIM provide developers with secure access to a wide array of models and performance-optimized inference microservices.
By circumventing the latency, cost, and compliance issues associated with cloud-hosted APIs, as well as the complexities of model deployment, developers can concentrate on application development. This expedites the delivery of production-ready generative AI applications, facilitating seamless scale-out with performance optimization in data centers and the cloud.
The recent general availability of the Meta Llama 3 8B model as a NIM, which can run locally on RTX systems, brings cutting-edge language model capabilities to individual developers. This enables local testing and experimentation without relying on cloud resources. By running NIM locally, developers can create advanced retrieval-augmented generation (RAG) projects directly on their workstations.
Local RAG involves implementing RAG systems entirely on local hardware, eliminating the dependency on cloud-based services or external APIs.
Developers can utilize the Llama 3 8B NIM on workstations with one or more NVIDIA RTX 6000 Ada Generation GPUs or on NVIDIA RTX systems to construct end-to-end RAG systems on local hardware. This setup leverages the full capabilities of Llama 3 8B, ensuring high performance and low latency.
By running the complete RAG pipeline locally, developers retain control over their data, ensuring privacy and security. This approach is particularly beneficial for developers creating applications requiring real-time responses and high accuracy, such as customer-support chatbots, personalized content-generation tools, and interactive virtual assistants.
Hybrid RAG integrates local and cloud resources to optimize performance and flexibility in AI applications. Through NVIDIA AI Workbench, developers can delve into the hybrid-RAG Workbench Project — an example application facilitating the execution of vector databases and embedding models locally while conducting inference using NIM in the cloud or data center, offering a versatile approach to resource allocation.
This hybrid setup enables developers to balance the computational load between local and cloud resources, optimizing performance and cost. For instance, local workstations can host the vector database and embedding models for swift data retrieval and processing, while more demanding inference tasks can be offloaded to robust cloud-based NIM inference microservices. This flexibility allows developers to seamlessly scale their applications, accommodating varying workloads and ensuring consistent performance.
NVIDIA ACE NIM inference microservices breathe life into digital humans, AI non-playable characters (NPCs), and interactive avatars for customer service through generative AI, running on RTX PCs and workstations.
ACE NIM inference microservices for speech — encompassing Riva automatic speech recognition, text-to-speech, and neural machine translation — deliver precise transcription, translation, and lifelike voices.
The NVIDIA Nemotron small language model is a NIM catered to intelligence, featuring INT4 quantization for minimal memory usage and supporting roleplay and RAG scenarios.
Incorporating ACE NIM inference microservices for appearance, such as Audio2Face and Omniverse RTX, enhances gaming characters with immersive visuals and offers users interacting with virtual customer-service agents more immersive experiences.
Exploring NIM for AI Development
As AI continues to advance, the ability to swiftly deploy and scale AI capabilities proves increasingly vital.
NVIDIA NIM microservices lay the groundwork for this new era of AI application development, fostering groundbreaking innovations. Whether crafting the next-gen AI-powered games, pioneering advanced natural language processing applications, or building intelligent automation systems, users can access these potent development tools at their disposal.
Getting started:
- Experience and engage with NVIDIA NIM microservices on ai.nvidia.com.
- Join the NVIDIA Developer Program for free access to NIM for testing and prototyping AI applications.
- Purchase a license for NVIDIA AI Enterprise with a complimentary 90-day evaluation period for production deployment, utilizing NVIDIA NIM to self-host AI models in the cloud or data centers.
Generative AI is revolutionizing gaming, videoconferencing, and interactive experiences across various sectors. Stay informed about the latest advancements and upcoming trends by subscribing to the AI Decoded newsletter.