GPUs: Explained

Summary

An IBM product manager explains at a beginner level what GPUs are, how they differ from CPUs (parallel vs. serial processing), and why businesses should rent GPU power in the cloud for high-performance computing workloads such as VDI and AI/machine learning.

Why it matters

As GPU demand surges with AI workloads, many enterprise buyers still confuse 'GPUs' with gaming hardware or fail to grasp the balance between cloud and on-premises setups. A provider-agnostic, clear introductory guide helps procurement and IT leaders structure their discussions with cloud providers. However, because this content is fundamentally IBM sales material, its value is limited to the very earliest stage of awareness.

Highlights

• CPUs execute tasks serially. GPUs, on the other hand, perform parallel processing across hundreds of cores. This makes them suitable for tasks with computational intensity that a standalone CPU cannot handle.
• Beyond gaming, GPUs now form the backbone of VDI (3D CAD, rendering), AI inference and training processes, and HPC clusters.
• Accessing GPUs via the cloud avoids the capital costs and rapid obsolescence of on-premises hardware. The pay-per-use model is ideal for sudden workloads. Bare metal, however, is more suitable for continuous and intensive usage.

Critical take

While the distinction between parallel and serial processing is a useful metaphor, it is oversimplified. Modern CPUs leverage SMT and vector units. GPU performance depends on memory bandwidth and interconnect topology at least as much as on core count. None of these are mentioned in the text. More importantly, the 'why cloud' section reads like an IBM sales pitch. No competitors are named, and no benchmark data is provided. The only 'evidence' is the claim that GPUs are renewed annually, making on-premises setup 'nearly unfeasible.' This claim ignores the real cost and data sovereignty arguments for owning hardware.