Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective
Summary: arXiv:2511.00739v3 Announce Type: replace
Abstract: Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on the fly. Due to diverse task execution needs, such serving heavily relies on heterogeneous CPU-GPU systems, with the majority of the external tools responsible for agentic capability either running on or being orchestrated by the CPU.
Introduction
As artificial intelligence continues to evolve, the concept of Agentic AI has emerged as a transformative force in the field. By enabling AI systems to perform tasks autonomously, these systems shift from being simple inferencing models to complex problem solvers capable of adapting to dynamic environments. This paper delves into the critical role of the CPU in optimizing the execution of Agentic AI workloads, a perspective often overshadowed by a focus on GPU capabilities.
Characterization of Agentic AI Execution
To comprehend the intricate demands placed on hardware by Agentic AI, the authors present a comprehensive characterization of agentic AI execution. This involves:
- Compile-Time Characterization: Identifying representative workloads that highlight the algorithmic diversity inherent in Agentic AI.
- Runtime Characterization: Analyzing end-to-end latency and throughput across different hardware systems to isolate architectural bottlenecks.
Identifying System Bottlenecks
Through the characterization process, various bottlenecks were identified, primarily affecting the CPU’s ability to effectively manage heterogeneous tasks. The focus on CPU-centric analysis revealed the following key challenges:
- Latency issues arising from inefficient CPU-GPU communication.
- Resource allocation imbalances when managing diverse workloads.
- Underutilization of CPU resources in scenarios where GPU processing is prioritized.
Proposed Optimizations
In light of the identified bottlenecks, the paper proposes two innovative scheduling optimizations:
- CPU-Aware Overlapped Micro-Batching (COMB): This method focuses on enhancing CPU-GPU concurrent utilization, leading to improved performance in homogeneous workload execution.
- Mixed Agentic Scheduling (MAS): Designed for heterogeneous workloads, MAS reduces skewed resource allocation, thereby optimizing total execution time across different request types.
Experimental Evaluations
The efficacy of the proposed optimizations was validated through rigorous experimental evaluations conducted on two distinct hardware systems. Results indicated significant performance improvements:
- COMB yielded up to 1.7x lower P50 latency in standalone homogeneous workload execution.
- Under homogeneous open-loop load, COMB achieved up to 3.9x/1.8x lower service/total latency.
- For heterogeneous open-loop load, MAS demonstrated a reduction in total latency for minority request types by up to 2.37x/2.49x at P50/P90 percentile.
Conclusion
This study underscores the importance of a CPU-centric approach to optimizing Agentic AI execution. By addressing the bottlenecks and proposing targeted scheduling optimizations, the research contributes valuable insights into enhancing the performance of AI systems in increasingly complex application scenarios.