GPU Basics: CPU Comparison and Cloud Benefits
Key Points
- A GPU (graphics processing unit) contains hundreds of cores that run computations in parallel, unlike a CPU’s few cores which process tasks serially.
- This parallel architecture lets GPUs handle compute‑intensive workloads that would overwhelm a CPU, acting as extra “muscle” for demanding applications.
- Nvidia and AMD are the primary GPU manufacturers, each offering chips tuned for specific use cases such as virtual desktop infrastructure (VDI), 3D CAD, movie rendering, and AI workloads.
- In VDI and other graphics‑heavy scenarios, GPUs enable high‑performance, low‑latency visual experiences from remote cloud servers, eliminating the need for local powerful hardware.
- Beyond gaming, GPUs are increasingly critical in fields like financial services, life sciences, healthcare, and artificial intelligence (machine learning and deep learning), making them essential for modern cloud‑based compute solutions.
Sections
- GPU vs CPU Basics - Alex Hudac explains what a GPU is, how it differs from a CPU in core count and parallel processing, and why GPUs are essential for compute‑intensive workloads, especially in cloud environments.
- AI, GPUs, and HPC in Cloud - The passage outlines machine learning and deep learning as AI’s core components, describes specialized GPUs for training and inference, explains how GPUs enhance high‑performance computing workloads, and argues that leveraging GPUs in cloud environments provides the necessary performance for compute‑intensive tasks.
- Cloud GPUs: Cost and Performance Benefits - The speaker explains how cloud‑based GPUs reduce waste by charging only for used resources, deliver superior performance, and allow companies to focus on outcomes, while also covering GPU fundamentals, CPU differences, and key use cases such as VDI, AI, and HPC.
Full Transcript
# GPU Basics: CPU Comparison and Cloud Benefits **Source:** [https://www.youtube.com/watch?v=LfdK-v0SbGI](https://www.youtube.com/watch?v=LfdK-v0SbGI) **Duration:** 00:07:30 ## Summary - A GPU (graphics processing unit) contains hundreds of cores that run computations in parallel, unlike a CPU’s few cores which process tasks serially. - This parallel architecture lets GPUs handle compute‑intensive workloads that would overwhelm a CPU, acting as extra “muscle” for demanding applications. - Nvidia and AMD are the primary GPU manufacturers, each offering chips tuned for specific use cases such as virtual desktop infrastructure (VDI), 3D CAD, movie rendering, and AI workloads. - In VDI and other graphics‑heavy scenarios, GPUs enable high‑performance, low‑latency visual experiences from remote cloud servers, eliminating the need for local powerful hardware. - Beyond gaming, GPUs are increasingly critical in fields like financial services, life sciences, healthcare, and artificial intelligence (machine learning and deep learning), making them essential for modern cloud‑based compute solutions. ## Sections - [00:00:00](https://www.youtube.com/watch?v=LfdK-v0SbGI&t=0s) **GPU vs CPU Basics** - Alex Hudac explains what a GPU is, how it differs from a CPU in core count and parallel processing, and why GPUs are essential for compute‑intensive workloads, especially in cloud environments. - [00:03:24](https://www.youtube.com/watch?v=LfdK-v0SbGI&t=204s) **AI, GPUs, and HPC in Cloud** - The passage outlines machine learning and deep learning as AI’s core components, describes specialized GPUs for training and inference, explains how GPUs enhance high‑performance computing workloads, and argues that leveraging GPUs in cloud environments provides the necessary performance for compute‑intensive tasks. - [00:06:29](https://www.youtube.com/watch?v=LfdK-v0SbGI&t=389s) **Cloud GPUs: Cost and Performance Benefits** - The speaker explains how cloud‑based GPUs reduce waste by charging only for used resources, deliver superior performance, and allow companies to focus on outcomes, while also covering GPU fundamentals, CPU differences, and key use cases such as VDI, AI, and HPC. ## Full Transcript
Hi, my name is Alex Hudac. I'm an
offering manager at IBM and today I'm
going to talk to you about what is a
GPU. So, I get some pretty basic
questions on GPUs and that's what I'm
going to go over today. First question
is what is a GPU? What is the difference
between a GPU and a CPU? So, I'm going
to represent those here
and then lastly,
why use a GPU? And is it even important
to use a GPU on cloud? So let's start
first with what is a GPU. GPU stands for
graphic processing unit. Uh but
oftentimes people are more familiar with
CPUs. So CPUs are actually made up of
just a few cores. You can think of these
cores as the power or the ability of a
CPU to do certain calculations or
computations. On the other hand though
GPUs are made up of hundreds of cores.
But what difference does it make? So the
thing with a CPU is that when it does
the computation, it does so in a serial
form. So it does one computation at a
time.
But with a GPU, it does it in parallel.
So the importance of these two
differences is that with a GPU, you're
able to do computations all at once and
very intense computations at that. So
oftentimes when you have app codes, a
lot of it's going to be going to the
CPU,
but then every now and then you're going
to have an application that's going to
require quite a bit of compute intensive
uh support that the CPU just can't do.
So it's going to be offloaded to the
GPU. So you can think of a GPU as that
extra muscle or that extra brain power
that the CPU just can't do on its own.
So there are two main providers of GPUs
in industry, Nvidia and AMD. Both
providers manufacture GPUs that are
optimized for certain use cases. So
let's jump into that because big
question I get is why do I even need a
GPU? In what industries and in what use
cases? So the first we'll talk about is
VDI.
VDI stands for virtual desktop
infrastructure. So GPUs are created to
support highintensive graphic
applications. Uh so for think about if
you're a construction worker, right, and
you're out in the field and you need to
access a very high graphic intensive 3D
CAD program. So rather than having the
server right next to you, right in the
field with you, you can have a server
that's in a country away in a cloud data
center and be able to view that 3D
graphic as if that server was right with
you. and that's going to be supported by
the GPU because the GPU supports graphic
intensive applications. Another example
of this would be movie animation or
rendering. So in fact GPUs actually
first got their name mainly with the
gaming industry. Oftentimes they were
referred to as gaming processing units
because of this ability to provide uh
end users with low latency graphics. But
gaming is no longer the focus in
industry anymore. It's big piece of it,
but now financial services, life
sciences and even healthcare are
starting to get into it with artificial
intelligence.
So artificial intelligence has two big
pieces to it. There's machine learning
and there's deep learning. So now there
are also GPUs that are optimized and
created specifically for those
applications. So there are some that are
created for inferencing for machine
learning purposes and there are some
that are created to help data scientists
create and train neural networks. In
other words, they're trying to create
these algorithms that can think like a
human brain. That's something that a CPU
can simply not do on its own and
requires GPU capabilities.
And then lastly, let's talk about HPC.
HPC is a buzzword that's been going
around. stands for high performance
computing. Uh while a GPU is not
absolutely necessary for HPC, it's an
important part of it. So high
performance computing is a company's
ability to spread out their compute
intensive workloads amongst multiple
compute nodes or in the case of cloud
servers. Oftentimes though, these
applications are very compute inensive.
It could include rendering. It could
include AI and that's where a GPU comes
in. You can add a GPU to these servers
that are spread out amongst an HPC
application and utilize those in that
manner. So this is a nice little segue
into why should we use GPUs on cloud? If
HPC is such a big piece of that, what
else is important about cloud?
So the first part of that is you get
high performance.
You need cloud for that. The GPUs are
great but not on their own. So back in
the day and even still today there are
companies that use a lot of on-prem
infrastructure and they utilize that
infrastructure for any of their compute
intensive applications. However,
especially in the case of GPUs, the
technology is everchanging. In fact,
there's typically a new GPU coming out
almost every single year. So it's
actually very expensive and nearly
impractical for companies to keep up
with the latest technology at this
point. So cloud providers actually have
the ability to continually update their
technology and provide GPUs to these
companies to utilize them when they need
them. So on a more granular basis though
cloud technology can often be broken
down from an infrastructure perspective
between bare metal and virtual servers.
So let's talk about the differences.
There are advantages of using a GPU on
both types of infrastructure. If you
utilize a a GPU on a bare metal
infrastructure, the companies oftentimes
have access to the entire server itself
and they can customize the
configuration. So this is great for
companies that are going to be really
utilizing that server and that GPU
intense application on a pretty
consistent basis. But for companies that
need a GPU, maybe just in a burst
workload uh scenario, the virtual server
option might be even better. And the
nice thing about virtual is that there
are often different pricing models as
well, including hourly.
And the cool thing about cloud is that
you only pay for what you use.
So if a company is using on-prem
technology or infrastructure, but
they're not utilizing it at the time,
that technology is depreciating and it's
essentially a waste of money for that
company. So it just makes a lot more
sense from a cost perspective. And then
because the GPU is so great at
performance, it just makes sense from a
performance perspective as well. So
companies are able to focus way more on
output than they are on keeping up with
the latest technology. So in summary,
what we covered is what is a GPU graphic
processing unit, what the differences
between a GPU and a CPU, the use cases
for GPUs being in VDI, AI, and HPC, and
why is it even important for GPUs to be
used on cloud. Thank you guys for
joining me here today to learn about
what is a GPU. If you'd like to learn
more about GPUs, click on the links
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