How to self-host and hyperscale AI with Nvidia NIM

Summary

In this insightful video, the host explores the future of AI workforce with the advent of Nvidia's Nim, a tool that simplifies the deployment and scaling of AI models on Kubernetes. Highlighting the potential of specialized AI agents to transform industries, the video demonstrates how Nim can be utilized to create a seamless, efficient AI-driven workforce, from customer service to programming, emphasizing the augmentation rather than replacement of human labor. The host's personal ambition to build a billion-dollar business as a solo developer is made more plausible with the ease and efficiency of deploying AI models using Nim.

Takeaways

• 🚀 Nvidia has released a tool called Nim, which is an inference microservice for AI models, making it easier to scale AI applications.
• 🔮 The future workforce is predicted to be heavily influenced by AI, with many jobs being automated by robots.
• 🧠 AI models like LLM, Mistral, and Stable Diffusion are already changing the world but have not yet become mainstream.
• 💾 Nim packages AI models with necessary APIs for inference, including engines like Tensor RT and data management tools, all containerized for easy deployment.
• 🛠️ Nvidia's H100 GPU was used to demonstrate the capabilities of Nim, showcasing its ability to handle large-scale AI workloads.
• 🌐 Nims can be deployed in various environments, including the cloud, on-premises, or even locally, saving development time and effort.
• 🤖 The script humorously suggests replacing human roles with AI agents for various tasks, emphasizing the potential for AI to augment human work rather than replace it.
• 💻 The video provides a practical example of how to use Nim with Python, demonstrating the ease of accessing and utilizing AI models through an API.
• 🔧 Nvidia's platform includes a playground for experimenting with Nims, offering a range of models for different specialized tasks.
• 🔄 The use of Kubernetes allows for automatic scaling and healing of microservices, which is crucial for handling increased traffic and maintaining service reliability.
• 🛑 The video emphasizes the importance of latency in AI services and how tools like Triton help maximize performance without requiring users to be experts in optimization.

Q & A

What is the significance of the H100 GPU in the context of the video?

The H100 GPU is significant because it provides the computational power necessary to run and scale AI models efficiently, enabling the self-hosting of an 'Army of AI agents' as mentioned in the script.

What does the term 'Nim' refer to in the video?

In the context of the video, 'Nim' refers to inference microservices provided by Nvidia, which package AI models along with necessary APIs for running them at scale, including inference engines and data management tools.

How does the video suggest AI will change the workforce in the next 10 years?

The video suggests that AI will transform the workforce by automating jobs that can be done by a robot, with a potential shift towards a network of highly specialized AI agents running on platforms like Kubernetes.

What is the role of Kubernetes in deploying AI models as described in the video?

Kubernetes is used to containerize and deploy AI models, allowing for easy scaling and management of these models in various environments, including the cloud, on-premises, or local PCs.

What are the technical challenges mentioned in running AI models at scale?

The video mentions the need for massive amounts of RAM and the parallel computing capabilities of a GPU for running inference with AI models. Additionally, scaling up this technology has traditionally been difficult.

How does Nvidia Nim address the issue of scaling AI models?

Nvidia Nim addresses scaling issues by providing containerized AI models that can be deployed on Kubernetes, which allows for automatic scaling when traffic increases and self-healing when issues arise.

What is the purpose of the playground mentioned in the video?

The playground is a feature that allows users to interact with and experiment with various AI models, such as large language models and image/video processing models, directly in the browser or via API.

What is the potential impact of AI models like 'llama 3', 'mistol', and 'stable diffusion' on mainstream consciousness as per the video?

The video suggests that while these AI models have already changed the world, they have barely penetrated mainstream consciousness, indicating that there is significant potential for further impact as these models become more widely recognized and utilized.

How does the video illustrate the practical application of AI models in a business scenario?

The video uses a hypothetical scenario of 'Dinosaur Enterprises' where AI models are deployed to replace various human roles, such as customer service agents, warehouse workers, product managers, and even a mental health AI for the remaining human workforce.

What programming perspective is provided in the video regarding the use of Nvidia's H100 GPU and AI models?

The video demonstrates how to use Python scripts to interact with AI models running on an H100 GPU, including using HTTP requests to access the models and the Open AI SDK for ease of integration.

What is the significance of the Open AI SDK mentioned in the video?

The Open AI SDK is significant as it has become an industry standard for interacting with AI models, providing a familiar and widely adopted interface for developers working with AI.

Outlines

00:00

🚀 Future of AI Workforce with Nvidia Nim

This paragraph introduces the concept of a future workforce powered by AI, where mundane tasks are automated by robots, and more complex tasks are handled by specialized AI agents. The speaker discusses the potential of AI models like Llama 3, Mistral, and Stable Diffusion, and how they are yet to fully impact mainstream consciousness. The video aims to explore a future where AI is integral to every job, and mentions the challenges of scaling AI models, which require significant computational resources. Nvidia's Nim is introduced as a solution to these scaling issues, offering inference microservices that package AI models with necessary APIs for scalable deployment on Kubernetes, thus facilitating the development of AI workforces for various roles, from doctors to programmers.

05:01

🛠️ Implementing AI at Scale with Nvidia Nims

The second paragraph delves into the technical aspects of implementing AI at scale using Nvidia Nims. It describes the process of using an H100 GPU to run inference microservices and the ease of deployment thanks to containerization with Kubernetes. The speaker provides a real-world example of how AI can replace certain human roles in a company, such as customer service agents and warehouse workers, by deploying specific AI models. The paragraph also touches on the playful aspect of AI, with the speaker jokingly suggesting the replacement of product managers with AI that can generate product mockups. The focus then shifts to the practical implementation, showing how to use Python to interact with the AI models available through the Nvidia Nim platform, highlighting the ease of use and the performance optimizations provided by tools like Triton. The speaker concludes by emphasizing the importance of latency in AI SaaS products and the monitoring capabilities of the hardware used for AI deployment.

Keywords

💡H100 GPU

The H100 GPU, as mentioned in the script, refers to a high-performance graphics processing unit (GPU) from Nvidia. It is particularly powerful and is used in data centers. In the context of the video, the H100 GPU is used to demonstrate the capabilities of hosting and scaling AI agents, which is central to the theme of advanced AI and its potential impact on the workforce.

💡Nvidia Nim

Nvidia Nim is a term used in the script to describe inference microservices provided by Nvidia. These microservices package AI models with necessary APIs for scalable operation, including tools for inference, data management, and monitoring. The script emphasizes how Nims facilitate the deployment of AI models in various environments, which is key to the video's exploration of AI's role in the future workforce.

💡AI Workforce

The AI workforce concept in the script refers to a network of specialized AI agents capable of performing various professional roles, such as a doctor, lawyer, or programmer. The video discusses how these AI agents could potentially replace human workers in certain jobs, illustrating a future where automation is prevalent in the workforce.

💡Kubernetes

Kubernetes is an open-source container-orchestration system for automating application deployment, scaling, and management. In the video, it is mentioned as the platform on which the AI models, packaged as Nims, are containerized and run, allowing for easy deployment and scaling, which is crucial for the operation of the envisioned AI workforce.

💡LLM (Large Language Model)

LLM, or Large Language Model, is a type of AI model that is capable of understanding and generating human-like text. In the script, models like 'llama 3' and 'mistol' are mentioned as examples of LLMs that can be used within the Nim platform to perform tasks such as generating text for customer service or creating product mockups.

💡Stable Diffusion

Stable Diffusion is an AI model mentioned in the script that is capable of generating images and videos. It is used as an example of how AI can be employed to create visual content, such as product mockups or website designs, which could replace the need for human designers in the future.

💡AGI (Artificial General Intelligence)

AGI, or Artificial General Intelligence, refers to a hypothetical AI that possesses the ability to understand, learn, and apply knowledge across a broad range of tasks at a level equal to or beyond that of humans. The script speculates on the creation of a Sci-Fi AGI as a 'jack of all trades' that could perform any intellectual job better than humans, highlighting the potential advancements in AI technology.

💡Inference

Inference in the context of AI refers to the process of making predictions or decisions based on learned patterns from data. The script discusses how AI models require resources like massive RAM and GPU power to perform inference tasks, such as generating text or images, which is a fundamental aspect of deploying AI models at scale.

💡Tensor RT

Tensor RT is an SDK from Nvidia that is optimized for deep learning inference on Nvidia GPUs. It is mentioned in the script as part of the tools used by Nvidia Nim to maximize performance on inference tasks, demonstrating the importance of optimization for AI deployment.

💡API (Application Programming Interface)

APIs are sets of protocols and tools for building software applications, and in the script, they are described as being packaged with AI models in Nims to facilitate their operation. The video explains how APIs are used to interact with the AI models, such as making HTTP requests to access their capabilities.

💡Solo Developer

A solo developer refers to an individual who develops software independently, without being part of a team or company. The script mentions the personal goal of creating a billion-dollar business as a solo developer, using tools like Nvidia Nim to reduce development time and augment capabilities, illustrating the empowerment of individuals through advanced AI tools.

Highlights

Access to an overpowered H100 GPU enabled self-hosting and scaling of an AI agent army.

Future workforce predicted to be drastically different, with AI models like llama 3, mistal, and stable diffusion changing the world.

The mainstream has yet to fully embrace the transformative potential of advanced AI models.

Fast-forward to a future where AI does any job capable of being automated.

Speculation about the creation of a Sci-Fi AGI capable of outperforming humans in every intellectual job.

A more realistic vision involves a network of highly specialized AI agents running on Kubernetes.

Technical challenges in deploying AI models at scale due to RAM and GPU requirements.

Introduction of Nvidia Nim, simplifying the deployment and scaling of AI models.

Nvidia Nims package AI models with necessary APIs for scalable inference, managed through containerization on Kubernetes.

Nvidia's playground allows experimentation with various Nims in the browser or via API.

Nims facilitate the creation of an AI workforce for various roles, including doctors, lawyers, and programmers.

Nims reduce development time and augment human capabilities, making solo development of billion-dollar businesses more feasible.

Programming demonstration using an H100 GPU to interact with AI models via Docker and Kubernetes.

Nvidia SMI and Kubernetes used for monitoring and automatic scaling of AI microservices.

Python script example for interacting with AI models, showcasing ease of use and rapid response times.

Use of Triton to enhance performance on inference, eliminating the need for manual optimization.

Open AI SDK as an alternative to manual HTTP requests for interacting with AI models.

Nims provide an API capable of scaling to an infinite number of GPUs across various platforms.

Transcripts

recently somehow I got access to an

overpowered h100 GPU and on it I was

able to self-host and scale my own Army

of AI agents thanks to a new tool called

Nim 10 years from now the workforce will

look nothing like it does today Bill

Gates once said most people overestimate

what they can do in one year and

underestimate what they can do in 10

years AI models like llama 3 mistal and

stable diffusion have already changed

the world but they've barely even

penetrated the mainstream Consciousness

over the last year in today's video

we'll fast forward 10 years into the

future to a magical time when any job

that can be done by a robot will be done

by a robot some experts think that we'll

create a Sci-Fi AGI an all-in-one jack

of all trades in a black box that can do

every intellectual job better than we

humans do but that's highly speculative

perhaps a far more realistic vision is a

network of Highly specialized AI agents

running on kubernetes if you're an indie

hacker entrepreneur or even a massive

Enterprise and you want to build an AI

Workforce that includes a doctor a

lawyer and a programmer you'll quickly

run into a massive technical challenge

even if your AI models are smart enough

to do these jobs a model is quite

literally just a file with weights and

biases AKA numbers but in order to run

inference with it like generate text and

images you'll need a massive amount of

RAM and the parallel Computing magic of

a GPU to do all that linear algebra and

if your app ever goes viral it'll

quickly grind to a halt because scaling

up this technology is extremely

difficult well not anymore thanks to

Nvidia Nim the sponsor of today's video

Nvidia was kind enough to give me access

to an H1 100 GPU to try out their Nvidia

Nims which are inference microservices

what they do is package up popular AI

models along with all the apis that you

need to run them at scale including

inference engines like tensor RT llm as

well as data management tools for

authentication health checks monitoring

and so on all these apis along with the

model itself are containerized and run

on kubernetes that means you can deploy

it to the cloud on Prem or even on your

local PC and that's going to save you

weeks if not months of painful

development time well at a real example

in just a minute but what's cool about

this platform is that there's a

playground where you can play around

with these Nims right now it has all the

popular large language models like llama

mistal Gemma and so on it can do image

and video with stable diffusion and

others along with a bunch of other

specialized models for healthcare

climate simulation and more these models

are hosted by Nvidia and you can use

them right now in the browser or you can

access them via the API and they've been

standardized to work with the open AI

SDK in addition because it's

containerized you can also pull it with

Docker and run it in your local

environment or configure it in the cloud

to scale to any workload and now we can

start to see what the future Workforce

might look like imagine you work for

dinosaur Enterprises and your CEO

chainsaw Jeff wants to cut down the

human-based headcount by 90% is so it

can increase his bonus by 4% how is he

going to do that for shareholders so

first let's get rid of customer service

agents by deploying one Nim that can

recognize speech along with a large

language model to generate text we might

also want to replace warehouse workers

with Superior autonomous forlift drivers

and a custom trained Nim hosted on Prem

is perfect for that we also have

hundreds of worthless product managers

who do nothing but post Day in the Life

Tik toks so let's add a stable diffusion

Nim to generate product mockups and

website designs to get rid of them now

these websites aren't going to build

themselves well no actually they are if

we deploy a Nim that can code and then

finally for the last 10% of humans

working here we can deploy a mental

health Nim to ensure their continued

well-being now obviously I'm joking here

and humans will continue to thrive

thrive in the artificial intelligence

age but the main takeaway here is that

Nims allow anyone to scale AI in any

environment and it's all about

augmenting human work as opposed to

replacing it my personal goal is to

someday create a billion- Dollar

business as a single solo developer and

Nims are the perfect tool to make that

dream a little more realistic they

simultaneously reduce development time

while facilitating the deployment of

tools that augment my own limited human

capabilities but now let's take a look

at how it works from a programming

perspective like I mentioned before

Nvidia gave me access to an h100 for a

few days which is their 80 gb GPU used

in data C these things go for about 30

grand on the street if you can even get

your hands on one and it was just way

more horsepower than I even knew what to

do with as you can see here I have sshed

into a server which is also conveniently

running vs code in the terminal you'll

notice we've pulled a Docker image and

I'm also running Nvidia SMI to check the

status of the GPU there's also a running

process for kubernetes that will allow

this microservice to automatically scale

when traffic increases and automatically

heal when things break most importantly

though everything is configured to work

out of the box you don't actually have

to touch kubernetes yourself all we have

to do is write a little bit of python to

run the model you could do this in a

python notebook but I'm just going to

write a python script here in this

app.py file the actual API to access the

model is running on Local Host 8000 we

can use the request library and python

to send HTTP request to it like the

first thing we might want to do is see

which models are available in this

environment we currently have access to

llama 3 now that we have that piece of

information we can make a post request

to the chat completions endpoint and

most importantly we have an array of

messages here which provide the llm with

context for the conversation in my case

I want to ask it the question of what is

the best JavaScript framework of all

time then from there we can Define some

configuration options like the model

name max number of tokens temperature

Etc and then finally to get a response

all we have to do is make a post request

with this data now let's go ahead and

run this code by pulling up the terminal

and entering python app.py you'll notice

we get a full response almost

instantaneously under the hood these n

use tools you would expect like pytorch

but also other tools you may not know

about like Triton to maximize

performance on inference which is

awesome because that means you don't

need to figure out how to make things

fast on your own and I would say latency

is probably the number one killer for

people starting their own AI SAS

products oh and in addition we can also

monitor the hardware like here I can see

the GPU temperature jumped after I asked

it that question and we can also keep an

eye on the CPU and memory usage now of

course when I ask llama 3 for the best

JavaScript framework it's going to

respond with react even though that's

clear clearly a lie so I changed my

prompt to ask it what the worst

JavaScript framework is and of course it

threw shade at its arch nemesis angular

which is the real best JavaScript

framework ever invented but one other

thing I'll mention in the code here is

that instead of request we could also

use the open AI SDK which is extremely

popular and has become somewhat of an

industry standard the bottom line though

is that we now have an API that can

scale up to an infinite number of gpus

those gpus could live on AWS they could

live in your own data center or it could

be the one in your PC right now but

pretty awesome and if you want to try

out Nims for yourself I'd recommend

going directly to the API catalog at

build. nvidia.com where you can easily

try them out or check out Nvidia AI

Enterprise if your goal is to operate at

a massive scale thanks for watching and

I will see you in the next one