Boosting Agentic AI Throughput with Intel Xeon CPUs

As organizations deploy larger numbers of agents and increasingly complex workflows, sustaining high throughput becomes a key determinant of operational efficiency. These results demonstrate that an Intel Xeon 6 processor-based instance provides a strong foundation for agentic AI, delivering higher throughput across real-world healthcare, manufacturing and financial services workflows and enabling organizations to process more agent-driven work on the same infrastructure.

Product and Performance Information:

Terminal Bench:

Intel Xeon 6: 1-instance m8id.12xlarge: 48 vCPU, 185 GB total memory, Ubuntu 26.04, 7.0.0-1006-aws, Terminal-Bench 2.0. Tested by Intel as of September 2026. Results may vary.

5^th Gen AMD EPYC: 1-instance m8a.12xlarge: 48 vCPU, 185 GB total memory, Ubuntu 26.04, 7.0.0-1006-aws, Terminal-Bench 2.0. Tested by Intel as of September 2026. Results may vary.

Arm v9.2A: 1-instance m9gd.12xlarge: 48 vCPU, 185 GB total memory, Ubuntu 26.04, 7.0.0-1006-aws, Terminal-Bench 2.0. Tested by Intel as of September 2026. Results may vary.

Test-Methodology:

Benchmark

Terminal-Bench + Harbor 0.16.1, terminus-2 agent. Deterministic fixture replays - A recorded Claude trajectory is replayed through a local proxy, so runs do no model inference and no network (identical work on every system); one canonical terminus on all systems; verifier enabled, all three instances ran all the tasks without any failures.

Load
One sandbox per physical core, pinned 1 core/task via cpuset_slot_pinner, memory local to the socket. 24 concurrent sandboxes. 60-minute run, refill setting -k 500 (slots stay full); each task cycles many times.

Images
Task images pre-built and staged; runs make no network access (LLM replaced by the replay proxy on :4001). Per-container thread cap = 1.

Primary metric
whole-box system throughput calculated as tasks completed per 60 minutes (primary).