How to Choose the Right Cloud GPU for AI Workloads
Picking a cloud GPU for an AI project usually starts with a list of model names that all sound alike: H100, H200, L40S, B200. The names say very little until you know what you plan to run on them. This guide explains what a cloud GPU is, what to compare before renting one, and how the common options differ for AI work.
What a Cloud GPU Is
A cloud GPU is a graphics processing unit you use over the internet instead of buying and installing it yourself. You rent it for as long as you need it, run your job, and release it. AI models are trained and served on GPUs because the work can be split into many small calculations that run in parallel, and renting is a common choice for teams that don’t want to buy and maintain that hardware.
Providers offer cloud GPUs in different ways. Some rent single instances on demand, and others offer clusters or longer commitments. Billing rules differ too, so read how a provider measures usage before you start.
Training, Fine-Tuning, and Inference Need Different Things
Training a model from scratch is the heaviest job. It needs a lot of GPU memory and often several GPUs working together.
Fine-tuning an existing model is usually lighter, but the model still has to fit in memory.
Inference means running a trained model to answer requests. It depends heavily on memory capacity and memory bandwidth, especially for large language models.
Knowing which of the three you are doing narrows the choice before you look at a single spec.
What to Compare Before You Rent
Start with GPU memory. A model has to fit in memory to run on a single GPU. If it doesn’t, the work has to be split across several GPUs, which adds complexity.
Then look at memory bandwidth, which decides how quickly data moves to the processor. It matters most for inference on large models.
Also check how many GPUs you need and whether they are connected well enough for multi-GPU jobs. Finally, look at availability, the data center location, and how billing works.
How the Common Cloud GPUs Differ
The H100 and H200 are both built on NVIDIA’s Hopper architecture. The main difference is memory. The H100 SXM has 80GB of HBM3 at 3.35 TB/s, while the H200 has 141GB of HBM3e at 4.8 TB/s. That extra memory helps when models or context lengths are large.
The B100, B200, and B300 belong to the newer Blackwell generation and are made for large-scale training and inference. Memory differs by model and server configuration, so check the exact listing before you plan around a number.
The L40S and RTX 6000 Ada each carry 48GB of GDDR6 memory. The RTX 6000 Pro, which NVIDIA sells as the RTX PRO 6000 Blackwell, carries 96GB of GDDR7. These cards use GDDR memory instead of the HBM used on the Hopper cards, and they are often looked at for inference and fine-tuning of smaller models.
Where to Find These GPUs in One Place
AITECH Cloud Network is one provider that lists all of the GPUs above in one place. Its GPUs are described as available for AI training and inference, and the site also mentions rendering and scalable deployment.
The Compute Marketplace shows the H100, H200, B100, B200, B300, L40s, RTX 6000 Ada, and RTX 6000 Pro. Having the full range on one page makes comparison easier, since you can check the memory of each card against your model before you decide.
For a tier-by-tier view, AITECH Cloud Network’s guide to GPU cloud servers for AI groups cards into entry-level, mid-tier, and high-end options by workload.
How to Pick One Step by Step
- Decide whether the job is training, fine-tuning, or inference.
- Check the memory the model needs, using the model’s documentation as the starting point.
- Begin with the smaller card that fits. Move up only if the job needs more memory or more GPUs.
- Confirm availability and how billing is measured.
- Run a short test before committing to a long job.
What a Spec Sheet Can’t Tell You
Published specs are peak figures. Real speed depends on your software, batch size, data pipeline, and network. Two jobs on the same GPU can behave very differently. A short test on your own workload tells you more than any comparison table.
FAQ
1. Is a Cloud GPU the Same as a Dedicated Server?
Not always. Cloud GPUs are often rented as instances, while a dedicated server gives you a whole machine. Some providers offer both, so read what the listing includes.
2. How Much GPU Memory Do I Need for AI?
It depends on the model size and the precision you run it at. There is no single number. Start from the model’s own requirements and leave some room for working memory.
3. Do I Need Several GPUs to Run an AI Model?
Not necessarily. Many models fit on one GPU, especially for inference. Several GPUs become necessary when a model is too large for one card’s memory or when training time matters.
