Hey, let’s talk about GPUs today.

So, "What exactly is a GPU?" We’re usually more familiar with CPUs because, well, that’s what runs our regular computers, right?

👉 What is a GPU?

GPU stands for Graphics Processing Unit. As the name suggests, it was originally designed to handle graphics — like animations, 3D rendering, gaming visuals — things like that.

But today, GPUs are doing much more than just graphics. They’re now used for things like AI, machine learning, scientific research, and more.

Types of GPUs:

Integrated GPUs: Built into the same chip as our CPU (e.g., Intel Iris, Apple M1/M2). Great for casual use, but limited for heavy workloads like training AI models.
Dedicated GPUs: Separate hardware with their own memory (VRAM). These are the real workhorses for gaming, 3D rendering, and machine learning. This is the type of GPU you’ll find powering most cloud workloads — from AI model training to high-end rendering — because integrated ones just don’t cut it.

This might be confusing, let’s go slow..

🧠 GPU vs CPU — What’s the difference?

Let’s compare it to something simple.

A CPU is like a smart worker who does one task at a time, really fast.
A GPU is more like a huge group of workers doing lots of small tasks at the same time.

The CPU usually has just a few cores — maybe 4, 8, or 16. A GPU? It can have hundreds or even thousands of tiny cores.

So, when we say CPU works serially, we mean it finishes one thing before moving on to the next. But a GPU works in parallel — it does many things at once. That’s why it’s perfect for tasks like rendering a complex video scene or training a machine learning model.

💡 Why do we even use a GPU?

Great question.

Most regular apps — browsing, typing, even running business software — are fine with just a CPU. But if our app needs a lot of computing power, that’s when the CPU needs backup — and that’s where GPUs come in.

We can think of the GPU as that extra brainpower the CPU calls in when things get really intense.

🔧 Who makes GPUs?

The two big players in the GPU world are NVIDIA and AMD. They design different GPUs depending on the use case — some for gaming, some for AI, some for servers, etc.

🚀 Where do we use GPUs?

Let’s go over some common real-world examples:

1. VDI – Virtual Desktop Infrastructure

Imagine being an engineer working on a complex 3D design, like a machine part, a car engine, or a building — using special software that needs a lot of power to run smoothly.

Now, normally, we'd need a high-end computer with a powerful GPU sitting right in front of us to do that. But those machines are expensive and not easy to carry around.

With VDI:

We can connect to a super-powerful computer that's sitting in a cloud data center — kind of like logging in to a powerful remote PC.
That remote machine has a GPU, which does all the heavy work (like rendering 3D graphics).
We control it from our laptop or regular PC, and it feels like we're working on a high-end computer — even though the power is coming from somewhere else.

So, whether we're at home, in the office, or on a job site, we can access our heavy-duty tools without needing a heavy-duty machine in front of us.

2. Gaming

This is where GPUs became famous. Gamers want smooth, high-quality graphics, and GPUs deliver that. In fact, they were once called “Gaming Processing Units” for that reason.

3. Artificial Intelligence (AI)

This is where GPUs really shine today. AI needs a ton of math and repetition — things like:

Training neural networks
Processing huge amounts of data
And GPUs can do all of that in parallel, super fast. CPUs just can't keep up here.

4. High Performance Computing (HPC)

HPC is used for huge, complex tasks that regular computers can’t handle — like weather forecasting, 3D movie rendering, or scientific research.

It works by combining many powerful servers to solve one big problem together — like a superteam of computers.

When you add GPUs, the system becomes even faster, especially for jobs needing millions of calculations, like AI or simulations.

👉 Think of HPC as a computer dream team, and GPUs as their secret superpower.

☁️ Why use GPUs in the cloud?

Let’s be real — GPUs are powerful but very expensive, and the tech moves fast — models get upgraded almost every year. Buying one today might mean it’s outdated next year.

That’s why companies are now using cloud-based GPUs. They don’t have to worry about upgrading hardware, maintenance, or downtime. Just rent the power when you need it, and scale up or down instantly.

🌐 Who Offers Cloud GPUs?

The big cloud players — offer a wide variety of GPU options:

AWS (Amazon Web Services) – Offers NVIDIA H100, A100, V100, and even AMD GPUs via EC2 instances.
Azure (Microsoft) – Supports NVIDIA GPUs (like A100, H100, and RTX) in their NC, ND, and NV series VMs.
GCP (Google Cloud Platform) – Offers GPUs like A100, L4, and others, with flexible instance sizes.
Oracle Cloud, IBM Cloud, Alibaba Cloud – Also provide competitive GPU instances.

We can choose based on our workload and budget.

But, What Are These GPU Names?

When we hear terms like H100, A100, V100, or RTX, these are model names for different types of GPUs — just like we have different models of smartphones (iPhone 12, 13, 14…).

Each GPU model is designed with a specific purpose or generation in mind.

🔷 NVIDIA – The Most Popular GPU Company

NVIDIA is the leader when it comes to high-performance GPUs. They have different product lines for different use cases:

🧠 NVIDIA H100 (latest and most powerful):

Think of it as the Ferrari of GPUs.
Designed for AI, machine learning, and supercomputing.
Used to train large models like ChatGPT or other advanced AI systems.

💪 NVIDIA A100:

Also very powerful, but slightly older than H100.
Great for training big machine learning models, data analytics, or scientific workloads.
Commonly used in both AI research and industry.

⚙️ NVIDIA V100:

Older than A100, but still widely used.
Good for high-performance computing (HPC), AI, and big data processing.
Think of it like a slightly older sports car — not the latest, but still really fast.

🎮 NVIDIA RTX Series (like RTX A6000, RTX 4090):

These are more like graphic-oriented GPUs used in gaming, 3D rendering, and even some AI work.
They’re commonly found in workstations and gaming rigs, but also offered in the cloud for design-heavy workloads.

🎯 NVIDIA L4:

Balanced GPU for video streaming, AI inference, and graphics rendering.
Good for lighter AI workloads or media processing.

🔶 AMD GPUs – The Main Alternative to NVIDIA

AMD (Advanced Micro Devices) also builds powerful GPUs, especially for HPC and cloud computing.

Their Instinct series (like MI250, MI300) is used for AI and scientific computing.
AMD is often chosen in large supercomputers and is becoming more cloud-friendly.
Less common than NVIDIA in cloud services, but still a solid, competitive option.

🧠 So Why Does This Matter?

Each of these GPUs has different performance levels and pricing. When using the cloud, we can pick the GPU that best fits our job — whether we're:

Training a chatbot
Running a weather simulation
Rendering a movie
Or even playing or streaming a game

We don’t need to buy these GPUs — just rent them when needed through cloud providers.

🖥️ Bare Metal vs Virtual Machines (VMs)

There are a few ways to use GPUs in the cloud, depending on how much control or flexibility we need:

🔹 Bare Metal Servers:

We get the entire physical machine to ourself.
Best for long-running, high-performance GPU workloads.
More control and customization.
Can be used for training large AI models over weeks.
Examples:
- DataCrunch, latitude.sh (H100)
- JarvisLabs.ai (A100)
- EXOSCALE, LeaderGPU (V100)
- vxstream, Cirrascale, Hivelocity, fasthosts, Hetzner (Non-NVIDIA options too)

🔹 Virtual Machines (VMs):

A virtual slice of a physical machine.
Ideal for short-term or burst workloads.
Easier to scale and more budget-friendly.
Can be used for testing a model or rendering graphics for a few hours.
Examples:
- Lambda, Scaleway (H100)
- Crusoe Cloud, Seeweb, Vultr (A100)
- OVHcloud, Paperspace (V100)
- CoreWeave, Linode, TensorDock, FluidStack (RTX)

If a company really needs the full power of the GPU 24/7, they might go for a bare metal server — which means they get the whole machine to themselves.

But if they just need GPU power every now and then — say, for a few hours a week — virtual servers make more sense. They can rent GPU time, and only pay for those hours.

⚙️ What about Serverless GPUs?

Serverless computing is when we don’t worry about the servers at all — we just run our code, and the cloud handles everything behind the scenes.

While true serverless GPUs are still evolving, services like:

Google Vertex AI
AWS SageMaker
Azure Machine Learning

...let us train models or run AI tasks without managing infrastructure. They automatically spin up the right GPU in the background and shut it down when we're done — super efficient.

Example tools in this category:

Banana.dev (H100)
Replicate, Modal, fal.ai (A100)
Baseten (V100)
RunPod (RTX)

🤔 So Why Does This All Matter?

If we're doing anything involving:

AI / ML model training
Deep learning
3D rendering
Scientific simulations
Video rendering
Game development

...we’ll eventually hit a point where CPUs just can’t keep up. That’s where GPUs — especially in the cloud — become our best friend.

Cloud GPUs give us:

Access to the latest hardware
Scalable pricing (pay-as-you-go)
No hardware maintenance
Deployment across the globe
Flexibility to run short tasks or long-term jobs

💸 What about cost?

One of the best things about GPUs in the cloud is the pricing model.

With on-prem servers, we’re stuck paying for hardware whether we use it or not. In the cloud, we only pay when we’re using it. So companies save money while still getting top performance.

🧾 Final Thoughts

So to sum it up:

CPUs are great for general tasks.
GPUs are the power lifters — made for parallel, compute-heavy work.
Today, GPUs are being used for more than just gaming — AI, research, 3D rendering, finance, and more.
And the cloud makes them easier and cheaper to use than ever before.

If we're building anything that needs serious processing power, chances are a GPU is our best friend.

Reference: GPUs: Explained

Everything You Need to Know About GPUs