Heterogeneous Computing: The Key to Powering the Future of AI Agent Inference

• URL: http://arxiv.org/abs/2601.22001v1
• Date: Thu, 29 Jan 2026 17:11:46 GMT
• Title: Heterogeneous Computing: The Key to Powering the Future of AI Agent Inference
• Authors: Yiren Zhao, Junyi Liu,
• Abstract summary: We introduce two metrics - Operational Intensity (OI) and Capacity Footprint (CF)<n>OI/CF can shift dramatically, with long context KV cache making decode highly memory bound.<n>We further hypothesize agent- hardware co design, multiple inference accelerators within one system, and high bandwidth, large capacity memory disaggregation as foundations for adaptation to evolving OI/CF.
• Score: 12.811151742829137
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: AI agent inference is driving an inference heavy datacenter future and exposes bottlenecks beyond compute - especially memory capacity, memory bandwidth and high-speed interconnect. We introduce two metrics - Operational Intensity (OI) and Capacity Footprint (CF) - that jointly explain regimes the classic roofline analysis misses, including the memory capacity wall. Across agentic workflows (chat, coding, web use, computer use) and base model choices (GQA/MLA, MoE, quantization), OI/CF can shift dramatically, with long context KV cache making decode highly memory bound. These observations motivate disaggregated serving and system level heterogeneity: specialized prefill and decode accelerators, broader scale up networking, and decoupled compute-memory enabled by optical I/O. We further hypothesize agent-hardware co design, multiple inference accelerators within one system, and high bandwidth, large capacity memory disaggregation as foundations for adaptation to evolving OI/CF. Together, these directions chart a path to sustain efficiency and capability for large scale agentic AI inference.

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