Question 1

How many GPUs do I need for AI training?

Accepted Answer

It depends on your model size, dataset, and performance goals.Small models (under 1B parameters) can often be trained on 1–4 GPUs. Medium models (1–10B parameters) typically require 8–32 GPUs. Large-scale LLMs (tens or hundreds of billions of parameters) can need 128+ GPUs working in parallel.

Question 2

What’s the difference between a GPU cluster and a CPU cluster?

Accepted Answer

A CPU cluster uses traditional processors optimized for sequential tasks, while a GPU cluster is designed for massive parallelism, ideal for machine learning, AI, and visualization workloads. GPUs handle thousands of threads simultaneously, making them far superior for deep learning, computer vision, and scientific computation. Most modern HPC systems use hybrid CPU–GPU architectures for the best of both worlds.

Question 3

Should I build my own GPU server cluster or use a cloud service?

Accepted Answer

If you need full control, data sovereignty, and long-term cost efficiency, building an on-premises GPU server cluster might make sense. However, setup and maintenance are expensive and time-consuming.

Question 4

What kind of networking and storage do GPU clusters use?

Accepted Answer

High-performance GPU clusters rely on low-latency, high-bandwidth networking such as InfiniBand or NVLink to keep GPUs synchronized during distributed training. For storage, they use parallel file systems like Lustre, BeeGFS, or Ceph, which allow multiple GPUs to access large datasets simultaneously without bottlenecks.

Question 5

Can GPU clusters be used for tasks beyond AI training?

Accepted Answer

Absolutely. While they’re most famous for deep learning, GPU clusters also power HPC simulations, 3D rendering, data analytics, video encoding, and scientific research. Any workload that requires high-speed parallel computation can benefit from HPC GPU cluster acceleration.