How to self-host and hyperscale AI with Nvidia NIM

How to Self-host and Hyperscale AI with Nvidia NIM

Introduction Recently, I gained access to a powerful H100 GPU which allowed me to self-host and scale my own army of AI agents with a new tool called Nvidia NIM. Over the next ten years, the workforce could undergo transformative changes, courtesy of AI models like Llama 3, Mistral, and Stable Diffusion. But what does this mean for scalability and deployment? In this article, we'll fast forward ten years to explore a future where any job that can be done by a robot, will be done by a robot.

The Future of AI and Workforce Bill Gates once said, "Most people overestimate what they can do in one year and underestimate what they can do in ten years." AI has already started making significant impacts yet has barely scratched the surface. In the future, we might not have a singular Sci-Fi AGI but rather a network of highly specialized AI agents running on Kubernetes.

Even if your AI models are advanced enough to undertake complex tasks, deploying them requires a lot of RAM and GPU parallel computing. This is where Nvidia NIM comes into play, simplifying the entire process from development to scaling.

Introducing Nvidia NIM Nvidia NIM offers inference microservices that package popular AI models with all necessary APIs for large-scale deployments. These include inference engines like Tensor RT, data management tools for authentication, health checks, monitoring, and more. The models and APIs are containerized and run on Kubernetes, easily deployable either on-premises or in the cloud.

Exploring the NIM Playground You can explore various models like Llama, Mistral, and even Stable Diffusion through the NIM playground. These models run on browsers or can be accessed via APIs. Whether for healthcare, climate simulation, or general AI tasks, these standardized models can be pulled via Docker and deployed on any environment.

Practical Workforce Application For example, consider "Dinosaur Enterprises," where the goal is to cut human-based roles by deploying AI agents:

Customer Service: A NIM that recognizes speech and generates text.
Warehouse Management: Autonomous forklift drivers via a custom-trained NIM.
Product Management: Stable Diffusion NIM generating product mockups and website designs.
Web Development: A NIM that automates coding tasks.
Employee Well-being: A mental health NIM.

This isn't suggesting that AI will replace humans entirely, but such augmenting tools can save considerable development time and facilitate workload automation.

Hands-on Example Nvidia offered access to an H100 GPU, an 80GB powerhouse, to try out NIMs. Connecting to a server and pulling a Docker image, I ran Kubernetes and checked the GPU status using Nvidia SMI. NIMs offered seamless out-of-the-box configurations, barely requiring manual tweaking.

Coding with NIM I wrote a simple Python script to interface with a model:

Pulled the Docker image and ran Kubernetes.
Wrote a Python script to interface with the API running on localhost.
Sent requests to the API to get model data and received nearly instantaneous responses.

These responses were facilitated by back-end tools like PyTorch and Triton, which ensure optimal performance and reduce deployment latency.

Monitoring and Optimization NIMs come with built-in monitoring for hardware performance. You can observe GPU temperature, CPU, and memory usage, ensuring your AI models run optimally.

Wrapping Up The flexibility to deploy AI models at scale—from personal GPUs to massive cloud infrastructures—opens unlimited possibilities for the future workforce.

Get Started To try out NIMs, visit Nvidia AI Enterprise and explore their API catalog at build.nvidia.com.

Keywords

Nvidia NIM
AI agents
AI models
Kubernetes
H100 GPU
Inference microservices
Scalability

Keywords

Nvidia NIM
AI agents
AI models
Kubernetes
H100 GPU
Inference microservices
Scalability

FAQ

Q: What is Nvidia NIM? A: Nvidia NIM offers inference microservices that package popular AI models along with essential APIs, allowing for large-scale deployments.

Q: What makes Nvidia NIM different from traditional AI deployment? A: Nvidia NIM simplifies the entire process by containerizing models and APIs, making them easily deployable on Kubernetes. It includes data management tools for authentication, health checks, and monitoring.

Q: What are some practical applications of Nvidia NIM in the workforce? A: Nvidia NIM can replace roles like customer service, warehouse management, product management, web development, and can even contribute to employee well-being through custom-trained models.

Q: How can I explore and use Nvidia NIM models? A: You can use the NIM playground to explore models or access them via APIs. Docker images can be used to pull models for local or cloud deployment.

Q: What tools ensure optimal performance for NIMs? A: Back-end tools like PyTorch and Triton are used to maximize performance, ensuring that your AI applications run efficiently.

Q: Where can I get started with Nvidia NIM? A: To start using Nvidia NIM, visit Nvidia's AI Enterprise at Nvidia AI Enterprise or explore the API catalog at build.nvidia.com.