Overview
Cloud GPUs help you scale fast, cut ops effort and stay on current hardware. To help you make better GPU buying decisions, this eBook turns raw specs into clear choices for real workloads.
- It shows how to size VRAM with simple rules and how to balance CPU and RAM, so pipelines never stall.
- You get practical guidance for AI training and inference, HPC simulations, rendering and visualization, VDI, gaming and large-scale video processing.
- It also explains when FP64 accuracy matters, how MIG partitions big GPUs and where NVLink improves multi-GPU scale.
Who Should Download this eBook?
We made sure this guide speaks to AI and ML engineers, DevOps and platform teams, data scientists and HPC practitioners who evaluate price-performance every week.
To make the information easy to digest, we ensured each chapter moves from concept to action. You will be able to validate decisions with a short benchmark before committing budget.
What Will You Learn?
You will learn to estimate training and inference memory, decide when RT cores change outcomes and choose host CPU threads and system RAM that keep GPUs saturated.
We have also added a compact comparison checklist to help you judge throughput, memory bandwidth and total cost with confidence.
Quick Picks to Get Started
- Pick H100 for the largest models and peak training speed.
- Choose A100 for big training or FP64-heavy HPC.
- Select L40S for mixed training with real-time graphics.
- Use A40 or RTX A6000 for visualization and moderate AI.
- Consider L4 or A10 when inference and video throughput per dollar matters.
- Go with A30 or A2 for balanced or lightweight tasks.
Download Now!
Ready to choose confidently? Download the eBook and get a practical path to the right GPU for your workload. If you prefer expert help, talk to AceCloud experts and we’ll help you map your use case to the best instance and pricing.
