Why it matters now

The rise of agentic AI is creating a new class of workloads that rely heavily on CPUs for coordination, planning and tool execution. TrendForce notes that these workloads can generate up to 90.6% of total latency in some cases, and that dynamic CPU energy use can become a large share of system power. As AI moves from single-shot responses to multi-step autonomous actions, the traditional GPU-centric model no longer delivers optimal performance.

1. Re-evaluate CPU-to-GPU ratios

TrendForce expects the ratio to shift from roughly one CPU for every four-to-eight GPUs to one CPU for every one to two GPUs. Review current deployments and model future demand using the 30 million to 120 million cores per gigawatt estimate cited by TrendForce.

2. Size for latency-critical paths

CPU-based tool processing can account for as much as 90.6% of total latency in agentic workloads. Identify latency-sensitive pipelines and provision enough CPU headroom to avoid bottlenecks that would negate GPU speed gains.

3. Plan for energy and cooling impact

TrendForce’s analysis shows CPU dynamic energy consumption can dominate under certain conditions. Factor the higher power draw of additional cores into data-centre cooling and power-budget calculations.

4. Deploy tightly coupled CPU and GPU platforms

Nvidia’s Grace processor, now on the Vera generation, combines 88 CPU cores with high-bandwidth NVLink-C2C links to its GPUs. Using such integrated systems can reduce inter-processor latency and improve overall efficiency.

What this means for enterprise IT teams

Teams must shift from a GPU-first mindset to a balanced architecture that treats CPUs as first-class citizens in AI pipelines. Capacity planning should incorporate the four-fold core increase forecast by TrendForce, and budgeting must reflect the higher power and cooling requirements.

In the words of TrendForce: "CPU-based tool processing can account for as much as 90.6% of total latency in some agentic workloads."

The bottom line

Agentic AI is set to revive the CPU’s role in data-centre AI stacks. Ignoring the shift will lead to performance gaps and rising costs. By reassessing ratios, addressing latency, planning for energy, and using integrated CPU and GPU platforms, enterprises can stay ahead of the demand surge.

Sources

  • networkworld.com