What Types of GPUs Are Typically Offered in GPU as a Service Solutions?

If there’s one thing that became clear in the last two years, it’s that the world is running on GPUs. From generative AI models and robotics to advanced analytics and real-time automation — everything today depends on high-performance GPU compute. Reports show that global GPU demand grew by 52% in 2024, largely driven by AI companies, enterprises migrating to cloud hosting, and research institutions shifting from traditional CPU servers to GPU-powered cloud infrastructure.

But here’s an interesting twist.
While GPU as a Service (GPUaaS) has become the most flexible way to access powerful GPUs without buying expensive hardware, very few users actually understand which GPUs they’re using — or which ones they should be using.

And that makes a huge difference.

Different GPUs offer different performance levels, memory capabilities, and power consumption patterns. Choosing the wrong GPU can slow down your project or increase your cloud bill dramatically. Meanwhile, choosing the right one can cut processing time by half and reduce overall costs.

So, let’s break down — in a clear, friendly, practical way — the different types of GPUs typically offered in GPU as a Service solutions, why they matter, and how they fit into real-world AI workloads.

Understanding GPU as a Service and Its Growing Popularity

Before diving into GPU categories, it’s worth understanding why organizations are choosing GPUaaS over traditional GPU servers.

GPU as a Service allows businesses to rent GPU compute resources through the cloud. No large upfront investment. No maintenance. No hardware failures. No cooling costs.

You get:

- On-demand GPU servers

- Pay-as-you-go pricing

- Scalable performance

- Access to the latest GPU models

- Flexibility to switch GPU types anytime

This model is particularly popular among AI startups, data science teams, universities, animation studios, and enterprises modernizing their cloud infrastructure.

But the availability of multiple GPU types can confuse users. So let’s simplify it.

Types of GPUs Typically Offered in GPU as a Service Solutions

Let’s divide GPU offerings into four main categories so you understand what each is built for, why they matter, and when you should pick one over another.

High-End GPUs for Large-Scale AI Training (A100, H100, GH200)

These GPUs are considered the “supercomputers” of the AI world.
If your organization is building large-scale AI models, these GPUs are the backbone of performance.

NVIDIA A100

The A100 became the industry standard for AI training between 2020–2023.
Cloud hosting providers still offer it widely because it balances power and cost.

Key features:

- 40–80 GB HBM2 memory

- High Tensor Core performance

- Ideal for GPU server clusters

- Excellent for deep learning training

- Strong multi-instance GPU (MIG) support

NVIDIA H100

Arguably the most coveted GPU in the world today.

H100 GPUs power almost every major AI model, including GPT, Llama, and Claude. These GPUs are expensive, but their performance often justifies the cost.

Highlights:

- Massive speed upgrade over A100

- Built for extremely large models

- Perfect for 6–7-figure parameter training

- Preferred for LLMs, medical imaging, and simulation

NVIDIA GH200 / Grace Hopper

This GPU+CPU hybrid is designed for next-generation AI compute.

It’s a newer addition to GPU as a Service platforms, ideal for AI-heavy cloud workloads.

When to choose high-end GPUs:

- Training LLMs

- Building diffusion models

- Running advanced simulations

- High-throughput multi-GPU clusters

- Enterprise-grade ML pipelines

Mid-Range GPUs for Training + Inference (L40S, V100, RTX 6000 Ada)

Not every workload needs the horsepower of an A100 or H100.
Many cloud GPU users choose mid-range GPUs because they provide a perfect balance of power, memory, and cost.

NVIDIA L40S

One of the most in-demand GPUs for cloud users in 2024–2025.

Why it’s popular:

- Great performance across training & inference

- Strong FP8 support

- More affordable than A100/H100

- Perfect for enterprise AI adoption

NVIDIA V100

Still widely used in GPUaaS platforms globally.

Although older, it remains powerful and reliable.

You should choose V100 if:

- You want consistent performance

- Your budget isn’t high

- You’re working on vision models or NLP models

RTX 6000 Ada Generation

A favorite among:

- Designers

- 3D animators

- Research labs

- Developers running mid-sized ML workloads

It offers excellent versatility without a huge price tag.

When to choose mid-range GPUs:

- Mid-sized ML training

- Reinforcement learning

- Video processing

- 3D workloads

- Development & testing environments

Entry-Level GPUs for Inference and Light AI Workloads (T4, L4, P4)

These GPUs may not be the fastest, but they are incredibly useful — and cost-efficient — for inference-heavy deployments.

NVIDIA T4

The most widely deployed GPU in cloud hosting environments around the world.

Why?
Because it offers:

- Balanced performance

- Excellent inference speed

- Low cost

- Low power consumption

Perfect for:

- Chatbot inference

- Image classification

- Small-scale AI applications

NVIDIA L4

A newer inference GPU with significantly better performance than T4.

Great choice for:

- Computer vision

- Real-time recognition

- Video analytics

NVIDIA P4

Older than T4 but still used in some affordable GPUaaS solutions.

When to choose entry-level GPUs:

- Inference-focused workloads

- Cost-sensitive projects

- Lightweight ML models

- Video streaming analytics

GPUs for Graphics, Rendering & Virtual Workstations (RTX Series, Quadro, M60)

GPU as a Service isn’t only for AI.
Creative industries rely heavily on GPUaaS for:

- VFX

- 3D modeling

- Game design

- Virtual workstations

- Rendering pipelines

NVIDIA RTX Series (RTX 3090, RTX 4090)

These GPUs pack incredible visual and compute performance for content creators and simulation developers.

NVIDIA Quadro Series

Designed for:

- CAD

- Architecture

- Industrial design

- Engineering simulations

Quadro GPUs are known for stability and long-term reliability.

NVIDIA M60 / GRID GPUs

Ideal for:

- Virtual desktops (VDI)

- Multi-user GPU environments

- Cloud-based workstations

When to choose graphics-focused GPUs:

- When rendering matters more than training

- When your project is not compute-heavy

- When you need visual accuracy and stability

How GPU Choices Affect Cloud Hosting & Server Performance

Choosing the right GPU doesn’t just impact model accuracy or runtime — it controls:

- Cloud hosting cost

- Memory usage

- Server scalability

- Training time

- Inference speed

- Energy efficiency

A business using H100 for a T4-level inference workload might spend 10–20 times more than necessary. On the other hand, underpowered GPUs can slow down your AI pipeline and delay deliverables.

This is why GPUaaS solutions offer a variety of GPU types — so users can pick exactly what they need.

How to Pick the Right GPU for Your Workload

Here’s a quick cheat sheet:

This is why understanding GPU types is essential — it affects everything from cost efficiency to performance speed.

Conclusion: GPU Variety in GPUaaS Is the Key to Smarter Cloud Adoption

As AI adoption grows, so does the need to choose the right GPU for the right job. GPU as a Service gives businesses the freedom to scale, experiment, and innovate without committing to expensive hardware — but choosing the wrong GPU can undermine performance and inflate cloud hosting costs.

High-end GPUs (A100, H100) power world-class AI training.
Mid-range GPUs (L40S, V100) balance cost and capability.
Entry-level GPUs (T4, L4) dominate inference workloads.
Graphics GPUs (RTX, Quadro) support creative and engineering pipelines.

When you understand these GPU categories clearly, optimizing your GPU server usage becomes far easier.