In the swiftly advancing world of artificial intelligence, generative AI is capturing imaginations and revolutionizing industries. Yet, behind the curtain, a crucial yet often overlooked element is driving these advancements microservices architecture.
NVIDIA NIM is a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across cloud, data centers, and workstations. NIM packages optimized inference engines, industry-standard APIs, and support for AI models into containers for easy deployment. So lets dive into the details as NVIDIA Introduces NIMS The Microservices Fueling Generative AI
NVIDIA NIM (NVIDIA Inference Microservices) is revolutionizing how developers run generative AI models by enabling local deployment on NVIDIA RTX AI workstations and GeForce RTX systems. With NIM, developers can operate advanced models like Meta Llama 3 8B entirely on local hardware, eliminating the need for cloud-based services or external APIs. This capability paves the way for building sophisticated retrieval-augmented generation (RAG) systems with unmatched performance and control. Read more such articles on Futureaitoolbox.com
NVIDIA Introduces NIMS The Microservices Fueling Generative AI
NVIDIA has unveiled a robust suite of enterprise-grade generative AI microservices designed to empower businesses to develop and deploy custom applications on their own platforms while retaining complete ownership and control of their intellectual property.
Built on the NVIDIA CUDA® platform, this comprehensive catalog of cloud-native microservices features NVIDIA NIM microservices optimized for inference across over two dozen popular AI models from NVIDIA and its ecosystem partners. Additionally, NVIDIA offers accelerated software development kits, libraries, and tools now accessible as NVIDIA CUDA-X™ microservices, catering to retrieval-augmented generation (RAG), guardrails, data processing, and high-performance computing (HPC). NVIDIA has also introduced a specialized collection of over two dozen healthcare-focused NIM and CUDA-X microservices.
This curated selection of microservices enhances NVIDIA’s full-stack computing platform, bridging the gap between AI model developers, platform providers, and enterprises. It provides a standardized pathway to deploy customized AI models optimized for NVIDIA’s CUDA installed base, spanning hundreds of millions of GPUs across clouds, data centers, workstations, and PCs.
Leading application, data, and cybersecurity platform providers, including Adobe, Cadence, CrowdStrike, Getty Images, SAP, ServiceNow, and Shutterstock, are among the first to leverage NVIDIA’s latest generative AI microservices through NVIDIA AI Enterprise 5.0.
“Enterprises with established platforms possess vast repositories of data ripe for transformation into generative AI companions,” said Jensen Huang, founder and CEO of NVIDIA. “Developed in collaboration with our ecosystem partners, these containerized AI microservices serve as foundational tools for companies across all industries to embark on their AI journey.”
Local Deployment on RTX Workstations/Systems
NIM allows developers to leverage the full power of NVIDIA RTX AI workstations and GeForce RTX systems to run generative AI models locally. This local deployment capability ensures developers can build and test applications without the constraints and dependencies of cloud services.
The Building Blocks of Modern AI Applications
Microservices architecture has emerged as a transformative force in software design, fundamentally altering how applications are constructed, maintained, and scaled. This innovative approach dissects an application into a suite of loosely coupled, independently deployable services. Each service is dedicated to a specific function and communicates with other services through well-defined application programming interfaces (APIs).
This modular structure sharply contrasts with traditional monolithic architectures, where all functionalities are tightly integrated into a single entity. By decoupling services, development teams can simultaneously work on different components, speeding up the development process and enabling independent updates without disrupting the entire application. This specialization fosters better code quality and quicker problem resolution, as developers can concentrate on mastering their specific domains.
Moreover, microservices can be scaled independently according to demand, enhancing resource efficiency and overall system performance. This flexibility also allows different services to utilize the most suitable technologies for their specific tasks, empowering developers to leverage the best tools available for optimal outcomes.
Getting Started
To begin using NIM, developers can join the NVIDIA Developer Program for free access to NIM for testing purposes. For production deployment, purchasing an NVIDIA AI Enterprise license provides a 90-day free evaluation period. The setup process involves configuring the NIM container, starting it, and integrating NIM endpoints into the application code.
Here are the key steps to get started with running NVIDIA NIM microservices locally on your NVIDIA RTX AI workstation or GeForce RTX system:
Prerequisites: Ensure you have an NVIDIA AI Enterprise license, which provides access to download and use NVIDIA NIM. You’ll also need an NVIDIA RTX workstation or GeForce RTX system with the necessary GPU hardware.
Set up the NIM container: Follow the steps outlined in the search results to set up the NIM container on your local system. This includes choosing a container name, selecting the NIM image from the NGC registry, and setting up a local cache directory.
Start the NIM container: Run the provided Docker command to start the NIM container, which will download and set up the required models and runtime components on your local machine.
Test an inference request: Once the container is running, you can test it by sending a sample inference request using the provided curl command. This will validate that the NIM microservice is working correctly on your local system.
Integrate NIM into your applications: The search results provide guidance on how to integrate the NIM endpoints into your application code, using frameworks like OpenAI, Haystack, LangChain, and LlamaIndex. This allows you to leverage the local NIM microservices in your own generative AI projects.
Simplifying GenAI Deployment with NIM
NVIDIA NIM (Inference Microservices) simplifies the deployment process for generative AI (GenAI) applications in several key ways:
Optimized Inference Engines: NIM provides pre-built containers with optimized inference engines like NVIDIA Triton, TensorRT, and TensorRT-LLM. This allows developers to easily integrate powerful AI models into their applications without having to worry about the complexities of model deployment and optimization.
Industry-Standard APIs: NIM exposes industry-standard APIs that developers can leverage to connect their GenAI applications to the available models. This abstracts away the underlying complexities and allows developers to focus on building their applications.
Simplified Deployment: NIM microservices can be deployed with a single command, making it easy to integrate into enterprise-grade AI applications. This accelerates the path to production for GenAI apps.
Flexibility and Scalability: NIM supports deployment across cloud, data centers, workstations, and laptops, providing flexibility. The underlying SUSE Enterprise Container Management stack enables efficient resource utilization and easy scaling of GenAI applications.
Security and Control: By running NIM models locally on NVIDIA RTX workstations and systems, developers can maintain complete control over data and ensure security and compliance, without relying on cloud-hosted APIs.
Observability and Monitoring: The integration of NIM with platforms like New Relic provides comprehensive observability and monitoring capabilities, helping organizations deploy cost-effective, high-performance GenAI models with confidence.
NVIDIA NIM simplifies the deployment of GenAI applications by providing optimized inference, standardized APIs, easy integration, flexible deployment options, enhanced security, and comprehensive observability – all of which accelerate the path to production for enterprises adopting generative AI.
Accelerate Deployments with NIM Inference Microservices
Experience a revolution in AI deployment times with NVIDIA’s NIM Inference Microservices. These cutting-edge microservices offer pre-built containers powered by NVIDIA’s leading inference software, including Triton Inference Server™ and TensorRT™-LLM, slashing deployment durations from weeks to mere minutes.
Designed with industry-standard APIs for domains such as language processing, speech recognition, and drug discovery, NIM microservices empower developers to swiftly build AI applications using their proprietary data securely hosted within their infrastructure. These applications are engineered to scale seamlessly on demand, delivering unmatched flexibility and performance on NVIDIA-accelerated computing platforms.
NIM microservices deliver the fastest and highest-performing AI containers for deploying models from top providers such as NVIDIA, A121, Adept, Cohere, Getty Images, and Shutterstock. They also support renowned open models from leading organizations like Google, Hugging Face, Meta, Microsoft, Mistral AI, and Stability AI.
ServiceNow has already leveraged NIM to expedite the development and deployment of domain-specific copilots and other innovative generative AI applications, driving faster time-to-market and cost efficiencies.
Customers can access NIM microservices through major platforms like Amazon SageMaker, Google Kubernetes Engine, and Microsoft Azure AI, seamlessly integrating with popular AI frameworks such as Deepset, LangChain, and LlamaIndex.
Introducing CUDA-X Microservices: Empowering Advanced AI Development
Experience a new era of AI innovation with NVIDIA’s CUDA-X microservices, offering comprehensive solutions for data preparation, customization, and training to accelerate production AI development across diverse industries.
Enhancing AI Adoption Across Industries
CUDA-X microservices provide essential building blocks, empowering enterprises to streamline AI adoption with specialized solutions such as:
NVIDIA Riva: Customizable speech and translation AI
NVIDIA cuOpt™: Routing optimization for efficient logistics
NVIDIA Earth-2: High-resolution climate and weather simulations
Revolutionizing AI Applications with NeMo Retriever™
NeMo Retriever™ microservices facilitate seamless integration of AI applications with business data, including text, images, and visualizations such as graphs and charts. This capability enhances the accuracy and relevance of responses from copilots, chatbots, and other generative AI tools.
Future-Ready AI Solutions from NVIDIA NeMo™
Upcoming NVIDIA NeMo™ microservices include:
NVIDIA NeMo Curator: Building clean datasets for training and retrieval
NVIDIA NeMo Customizer: Fine-tuning large language models (LLMs) with domain-specific data
NVIDIA NeMo Evaluator: Analyzing AI model performance
NVIDIA NeMo Guardrails: Ensuring compliance and governance for LLMs
Discover how CUDA-X microservices are reshaping AI development, paving the way for innovative applications across various sectors. Stay tuned for the latest advancements in NVIDIA NeMo™ microservices, empowering custom model development and AI performance analysis.
Empowering Enterprise Platforms with NVIDIA's Generative AI Ecosystem
Explore the dynamic ecosystem of NVIDIA’s generative AI microservices, where leading application providers, data platforms, and compute infrastructure partners converge to elevate enterprise capabilities.
Partnering for Enhanced AI Integration
Top data platform providers like Box, Cloudera, Cohesity, Datastax, Dropbox, and NetApp collaborate closely with NVIDIA microservices to optimize retrieval-augmented generation (RAG) pipelines and seamlessly integrate proprietary data into generative AI applications. Snowflake utilizes NeMo Retriever to harness enterprise data for developing advanced AI solutions.
Flexible Deployment Options
Enterprises can deploy NVIDIA microservices bundled with NVIDIA AI Enterprise 5.0 across their preferred infrastructure choices, including major cloud platforms such as Amazon Web Services (AWS), Google Cloud, Azure, and Oracle Cloud Infrastructure. These microservices are also supported on over 400 NVIDIA-Certified Systems™, spanning servers and workstations from industry leaders like Cisco, Dell Technologies, Hewlett Packard Enterprise (HPE), Lenovo, and Supermicro.
Advancing AI Solutions Across Industries
Today, HPE announced its enterprise computing solution for generative AI, integrating NIM and NVIDIA AI Foundation models to enhance AI software capabilities. NVIDIA AI Enterprise microservices are extending their reach to infrastructure software platforms such as VMware Private AI Foundation and Red Hat OpenShift, offering optimized capabilities for security, compliance, and control. Canonical is also facilitating Charmed Kubernetes support for NVIDIA microservices through NVIDIA AI Enterprise.
Expanding AI Partner Ecosystem
NVIDIA collaborates with a diverse ecosystem of over hundreds of AI and MLOps partners, including Abridge, Anyscale, Dataiku, DataRobot, Glean, H2O.ai, Securiti AI, Scale AI, OctoAI, and Weights & Biases. These partnerships integrate NVIDIA microservices into comprehensive AI solutions, enhancing scalability and performance across various domains.
Enabling Responsive AI Capabilities
Vector search providers such as Apache Lucene, Datastax, Faiss, Kinetica, Milvus, Redis, and Weaviate leverage NVIDIA NeMo Retriever microservices to power responsive RAG capabilities, enabling enterprises to deliver contextually relevant insights and enhance operational efficiencies.
Ensuring Security and Control in GenAI Applications with NIMS
NVIDIA NIM (Inference Microservices) helps maintain security and control over generative AI (GenAI) applications in several key ways:
Local Deployment on Secure Hardware: NIM allows developers to run GenAI models locally on NVIDIA RTX AI workstations and GeForce RTX systems, rather than relying on cloud-hosted APIs. This enables complete control over data and security, without exposing sensitive information to external services.
Optimized Inference Engines: NIM provides pre-built containers with optimized inference engines like NVIDIA Triton, TensorRT, and TensorRT-LLM. These engines are tuned for performance and security on NVIDIA’s accelerated hardware, ensuring robust and reliable inference.
Industry-Standard APIs: NIM exposes industry-standard APIs that developers can use to integrate GenAI models into their applications. This abstraction layer helps maintain control and security over the underlying models and infrastructure.
Simplified Deployment and Scaling: NIM microservices can be easily deployed and scaled using containerization and orchestration tools like Docker and Kubernetes. This enables enterprises to manage and secure GenAI applications at scale.
Observability and Monitoring: The integration of NIM with platforms like New Relic provides comprehensive observability and monitoring capabilities. This helps organizations detect and respond to security and performance issues in their GenAI applications.
Vulnerability Management: NIM containers include the latest security scanning results and provide access to NVIDIA’s Vulnerability Exploitability eXchange (VEX) documents to address any open-source vulnerabilities.
Compliance and Data Privacy: By running GenAI models locally on NVIDIA hardware, NIM enables enterprises to maintain complete control over their data and ensure compliance with relevant regulations and data privacy requirements.
NVIDIA NIM’s focus on local deployment, optimized inference, standardized APIs, simplified operations, observability, and vulnerability management helps enterprises deploy and manage GenAI applications with enhanced security and control over their data and models.
Industries That Benefit Most from NVIDIA NIM
The industries that can benefit the most from NVIDIA NIM include:
Healthcare: dozens of healthcare companies are deploying NIM to power generative AI inference across applications like surgical planning, digital assistants, drug discovery, and clinical trial optimization.
Finance, Insurance, and Asset Management: NIM can enable sophisticated generative AI applications like chatbots, virtual assistants, and sentiment analysis in industries like finance, insurance, and asset management.
Banking: NIM can power generative AI applications in banking, such as chatbots and virtual assistants, to improve customer experiences.
Customer Service: NVIDIA ACE NIM microservices, developers can easily build and operate interactive, lifelike digital humans for customer service applications.
Telehealth: NIM can be used to deploy generative AI-powered digital assistants and virtual consultations in telehealth applications.
Education: NVIDIA ACE NIM microservices can be used to build interactive, lifelike digital humans for educational applications.
Gaming and Entertainment: NIM’s capabilities in building digital humans can also benefit gaming and entertainment applications.
The key industries that can benefit the most from NVIDIA NIM include healthcare, finance, banking, customer service, telehealth, education, and gaming/entertainment, where generative AI can be leveraged to improve customer experiences, enhance productivity, and accelerate innovation.
Use Cases
NIM empowers a broad spectrum of generative AI applications, including:
Chatbots and virtual assistants
Content generation
Sentiment analysis
Language translation
These applications span various industries such as finance, insurance, asset management, and banking, enhancing their capabilities with advanced AI solutions.
NVIDIA NIM allows developers to harness the power of large language models like Meta Llama 3 8B locally on RTX workstations and systems. This enables the delivery of production-ready generative AI applications with high performance, low latency, and complete control over data privacy and security.
NVIDIA NIMS for Digital Humans
NVIDIA has introduced NIMS (Neural Inference Microservices) to help developers create highly realistic digital humans and characters
NIMS includes tools like NVIDIA Reva for speech recognition, NVIDIA Audio2Face for lip-syncing, and NVIDIA Omniverse RTX for real-time graphics
These tools enable creating digital humans with natural conversations, expressive faces, and lifelike animations
NIMS microservices can run on the cloud or locally on PCs with powerful GPUs for optimal performance
Many companies are using NIMS to power virtual assistants, interactive characters, and digital humans in gaming, customer service, healthcare and more
NVIDIA Robots and AI Factories
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NVIDIA is revolutionizing robotics with advanced AI models that can understand commands and execute complex tasks independently
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Robots learn skills by watching humans in NVIDIA’s Omniverse simulation platform, which combines real-time rendering, physics simulation, and generative AI
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NVIDIA AI supercomputers train the robots’ brains, while Jetson Orin and Thor chips act as the brains for real-world robot operation
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The future will see robots everywhere, from factories to consumer products, enabled by NVIDIA’s AI technologies
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NVIDIA is partnering with companies to build “AI factories” – data centers optimized for accelerated AI computing using CUDA, domain-specific libraries, and modular Blackwell systems
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The GB200 NVL2 chip is designed for data analytics with 18x faster data decompression and 8x better energy efficiency vs CPUs
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NVIDIA AI Enterprise software, including NIMS, makes it easier for companies to develop and deploy powerful AI solutions
NVIDIA NIMS and AI technologies are enabling the creation of highly realistic digital humans and robots, while powering the next generation of accelerated AI computing infrastructure. These advancements are poised to transform industries from gaming and customer service to manufacturing and robotics.
Benefits of Local NIMS
Running NIM locally offers several advantages:
Reduced Latency: Avoids the delays associated with cloud-hosted APIs.
Cost Efficiency: Eliminates the recurring costs of cloud services.
Compliance and Security: Maintains complete control over data, addressing compliance and privacy concerns.
High Performance: Leverages the full capabilities of large models for superior performance and low latency.
Real-Time Response: Ideal for applications requiring immediate and accurate responses.
Cost Savings with NVIDIA NIM
Using NVIDIA NIM can provide significant cost savings in deploying generative AI applications:
NIM leverages optimized inference engines for each model and hardware setup, providing the best possible latency and throughput on accelerated infrastructure. This helps reduce the cost of scaling inference workloads.
With NIM, businesses can optimize their AI infrastructure for maximum efficiency and cost-effectiveness without the complexities of AI model development and containerization.
In addition to providing accelerated AI infrastructure, NIM enhances performance and scalability, while also reducing hardware and operational costs.
The collaboration between New Relic and NVIDIA for AI monitoring of NIM-powered applications marks a significant milestone in terms of cost savings and a swifter path to ROI.
NIM’s ability to run generative AI models anywhere, from local workstations to cloud environments and on-premises data centers, provides flexibility and cost optimization.
By providing optimized inference engines, simplifying deployment, and enabling cost-effective infrastructure utilization, NVIDIA NIM can significantly reduce the costs associated with deploying and running generative AI applications at scale, while accelerating the path to ROI.
NVIDIA Introduces NIMS The Microservices Fueling Generative AI Final Thoughts
NVIDIA NIMS represents a pivotal advancement in the realm of generative AI, offering robust microservices that streamline deployment, enhance performance, and safeguard intellectual property. As businesses navigate the complexities of AI adoption, NIMS stands out for its ability to accelerate development cycles, optimize infrastructure costs, and deliver unparalleled control over data privacy and security.
As AI continues to evolve, NIMS remains at the forefront of enabling next-generation
AI applications. Whether powering digital assistants, enhancing customer experiences, or revolutionizing industrial processes, NIMS stands ready to accelerate the adoption of generative AI and shape the future of intelligent enterprise solutions.
Explore the possibilities with NVIDIA NIMS and discover how it can empower your organization to innovate, adapt, and thrive in the era of AI-driven transformation.
NVIDIA Introduces NIMS The Microservices Fueling Generative AI FAQs
What are NVIDIA NIMS?
NVIDIA NIMS are a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across cloud, data centers, and workstations. NIMS package optimized inference engines, industry-standard APIs, and support for AI models into containers for easy deployment.
What are the key benefits of NIMS?
The key benefits of NIMS include: 1) Simplified deployment and integration of generative AI models, 2) Optimized performance and scalability, 3) Flexibility to run on cloud, data centers, or local workstations, and 4) Enhanced security and control over data and models.
What types of generative AI applications can NIMS power?
NIMS can power a wide range of generative AI applications including chatbots, virtual assistants, content generation, sentiment analysis, language translation, digital humans, and more across industries like healthcare, finance, customer service, and gaming.
How do NIMS simplify the deployment of generative AI?
NIMS provides pre-built containers with optimized inference engines, industry-standard APIs, and support for popular AI models. This abstracts away the complexities of model deployment and allows developers to focus on building their applications.
Can NIMS run on local workstations and systems?
Yes, a key benefit of NIMS is the ability to run generative AI models locally on NVIDIA RTX workstations and GeForce RTX systems, without relying on cloud-hosted APIs. This enables complete control over data and security.
What types of NVIDIA hardware and software are NIMS compatible with?
NIMS are designed to run on NVIDIA-Certified Systems and can be deployed on leading cloud platforms as well as on-premises data centers. They integrate with NVIDIA AI Enterprise software and leverage NVIDIA’s CUDA, Triton Inference Server, and TensorRT-LLM technologies.
How do NIMS enable cost savings for generative AI deployments?
By providing optimized inference engines and simplifying deployment, NIMS helps reduce the hardware and operational costs associated with running generative AI workloads at scale. This accelerates the path to ROI for enterprises adopting these technologies.
What types of security and control features do NIMS offer?
NIMS enables local deployment on secure NVIDIA hardware, uses industry-standard APIs, provides comprehensive observability, and includes the latest security scanning and vulnerability management capabilities – all of which help enterprises maintain control and compliance over their generative AI applications.
Who are some of the partners integrating NIMS into their platforms?
Leading technology companies like Cadence, Cloudera, Cohesity, DataStax, NetApp, Scale AI, Synopsys, and Hugging Face are integrating NIMS into their platforms to speed up generative AI deployments for their customers.
How can developers get started with NIMS?
Developers can experiment with NIMS at ai.nvidia.com, join the NVIDIA Developer Program for free access, or purchase an NVIDIA AI Enterprise license which provides a 90-day evaluation period for production deployment of NIMS.