AI infrastructure is not about chasing the latest hardware trend — it’s about choosing the right tool for the job.

Akamai’s globally distributed edge network uniquely enables scalable, cost-efficient AI inference for real-time applications. By strategically using CPUs, Akamai further reduces costs and energy consumption — without compromising performance for many inference workloads.

Where to draw the line: CPU vs. GPU

Table 1 will help you make the right infrastructure choice based on your model architecture, latency requirements, and deployment environment.

Use central processing units (CPUs) when

Use graphics processing units (GPUs) when

Running real-time inference for lightweight or sparsified models

Training deep learning models or processing high-res media

Handling control logic or general purpose tasks

Needing massive parallelism (e.g., matrix operations)

Prioritizing cost-effective, widely available edge compute

Optimizing for raw performance in centralized data centers

Requiring…