Arm AGI CPU nearly doubles AMD EPYC in modelled agentic AI rack performance

Across the seven tests, Arm’s processor achieved an average performance ratio of 1.99x, with individual results ranging from 1.43x to 3.19x compared with AMD’s EPYC 9755. Some of the modelled advantage comes from lower server power consumption, allowing more Arm systems within the same electricity allocation. A 24-kW rack could accommodate 21 to 23 AGI CPU servers under the assessment’s assumptions, compared with 13 dual-socket EPYC 9755 systems.

That configuration provides between 5,712 and 5,888 physical cores for Arm, compared with 3,328 for AMD, though the comparison depends on rated power assumptions. Arm server power ranges from 950 to 1,050 watts, while AMD’s dual-socket configurations carry a rated consumption of 1,700 watts. By workload category, Arm recorded modelled rack-level advantages averaging 1.7x for tooling, 2.0x for agentic workflows and 2.2x for reasoning and planning. The report also estimated that database and vector-search operations could require 30% to 35% fewer racks for equivalent processing workloads.

Separate testing compared Arm’s processor against AMD’s EPYC 9965 using Sandbench, which measures the time required to establish isolated execution environments. Arm completed cold starts 45% faster and warm starts 27% faster, and its worst-case scenario under fourfold oversubscription was 41% faster. These improvements matter because AI agents frequently create execution environments while accessing services, running code and processing information. Faster setup can reduce delays, although the results do not demonstrate equivalent gains across all applications or complete data center deployments.

Mohamed Awad, Arm’s executive vice president for Cloud AI, said, “The industry needs to measure how well systems handle the work agents actually complete.” Arm developed its evaluation framework with support from Fujitsu, Meta and OpenAI, while Signal65 independently verified the reported processor results. Arm also acknowledged that further testing on final production systems would cover additional workloads and competitive comparisons.

For AMD and Intel, the findings suggest that Arm-based servers could challenge x86 infrastructure in agentic AI deployments where rack power limits constrain computing capacity. Higher rack-level performance could let operators accommodate more AI workloads within existing power allocations. However, the benchmark does not establish that Arm will outperform x86 across general-purpose computing or justify replacing existing servers across entire data centers. Software compatibility, deployment costs and real-world operating conditions will influence whether the modelled advantage translates into wider adoption.