TLDR: This research paper introduces a novel system design for deploying AI agent workloads on heterogeneous computing infrastructure, combining various CPUs and AI accelerators. By intelligently matching different parts of an AI agent’s task to the most cost-effective hardware, the system significantly reduces the Total Cost of Ownership (TCO) while maintaining performance. The approach leverages a sophisticated planning and orchestration framework, including MLIR for task representation, to dynamically allocate resources and optimize execution across diverse hardware, demonstrating that mixed hardware setups can outperform or match the efficiency of expensive homogeneous systems.
Artificial intelligence (AI) agents are rapidly transforming how businesses and consumers interact with technology. Unlike traditional software, these agents are dynamic and complex, often involving multiple AI models, data processing steps, and external tool interactions. As AI agent usage grows, there’s a critical need for efficient and scalable infrastructure to support them. Currently, most AI agent workloads run on expensive, uniform hardware, which limits their widespread adoption.
A new research paper, titled “Efficient and Scalable Agentic AI with Heterogeneous Systems,” by Zain Asgar, Michelle Nguyen, and Sachin Katti, addresses this challenge by proposing a system design for orchestrating AI agent workloads across diverse computing infrastructure. This includes a mix of CPUs and specialized AI accelerators from different vendors and performance levels. The core idea is to move away from expensive, one-size-fits-all hardware setups to a more flexible approach where each part of an AI agent’s task is matched with the most suitable and cost-effective hardware.
The paper highlights that AI agent tasks, such as converting speech to text, performing large language model (LLM) inferences, or making tool calls, have very different hardware requirements. For example, LLM inference might need powerful GPUs, while a simple data processing step could run efficiently on a CPU. By understanding these varied needs, the proposed system can intelligently assign tasks to hardware that offers the best balance of performance and cost.
The system introduces several key components to achieve this. It uses a framework for planning and optimizing AI agent execution, considering factors like compute power, memory, and data transfer needs of different hardware. It also employs a sophisticated representation system called MLIR (Multi-Level Intermediate Representation) to break down complex AI agent tasks into smaller, manageable operations. This allows the system to generate optimized code for various hardware options. Finally, a dynamic orchestration system places these smaller components across the heterogeneous infrastructure, ensuring they work together seamlessly while meeting performance targets.
One of the most compelling findings from their preliminary results is the significant reduction in Total Cost of Ownership (TCO) that heterogeneous infrastructure can deliver. Surprisingly, for some workloads, a combination of older generation GPUs (like H100) with newer accelerators (like Gaudi 3) can achieve similar cost efficiency as the latest generation homogeneous GPU setups (such as clusters of B200s). This suggests that existing GPU infrastructure could be utilized effectively for longer periods, delaying costly upgrades.
The system’s design includes a ‘slow-path’ planner and scheduler that continuously monitors hardware resources and workloads, making dynamic allocation decisions. A ‘fast-path’ load balancer routes requests efficiently. A runtime component on each accelerator node handles execution, memory management, and data collection. High-performance networking, like RDMA over Converged Ethernet (RoCE), is used for efficient data transfer between different hardware components, especially for large data like LLM key-value caches.
The use of MLIR is central to the system’s flexibility. It allows for a hierarchical representation of tasks, enabling operations to be combined or split for distributed execution. This also facilitates static analysis for scheduling, helping to estimate resource usage and latency, which feeds into the cost optimization framework. This approach means that high-level agent workflows, like those defined in frameworks such as LangChain, can be transformed into a structured format that the system can then optimize for heterogeneous hardware.
The preliminary evaluation focused on a conversational voice agent, demonstrating how different LLM inference stages (prefill and decode) can be placed on distinct hardware based on their computational needs. For instance, Intel Gaudi 3 accelerators proved cost-effective for prefill tasks with longer input sequences, while NVIDIA B200s were chosen when latency or FP8 performance was critical. This adaptability ensures optimal utilization of hardware capabilities, balancing cost efficiency with performance responsiveness.
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This research represents a crucial step towards building a more distributed and cost-effective AI infrastructure. It opens doors for future work in distributed datacenter scheduling, cross-device agent planning (spanning cloud and edge environments), and even the creation of an AI cloud marketplace where entities with spare AI compute capacity can offer their resources. For more technical details, you can refer to the full research paper here.
