Agent-X: Full Pipeline Acceleration of On-device AI Agents
Researchers have introduced Agent-X, a novel software-only framework designed to accelerate the performance of Large Language Model (LLM)-based agents on edge devices. While LLM agents offer state-of-the-art capabilities, they often suffer from high end-to-end latency when deployed on resource-constrained hardware. Agent-X addresses this by optimizing both the prefill and decode stages of agent workloads without compromising accuracy. The framework utilizes two primary components: prompt rewriting to leverage prefix caching tailored to agent-specific input patterns, and LLM-free speculative decoding for rapid token generation with minimal overhead. In tests on representative agentic workloads, Agent-X achieved a 1.61x end-to-end speedup in real systems with no loss in accuracy. The authors claim this is the first study to systematically characterize and eliminate latency bottlenecks in on-device agents. The solution is designed for seamless integration into existing on-device AI architectures, marking a significant advancement in efficient edge AI deployment. This development was published as a new preprint on arXiv under the Computer Science > Artificial Intelligence category.
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Agent-X: Full Pipeline Acceleration of On-device AI Agents
Researchers have introduced Agent-X, a novel software-only framework designed to accelerate the performance of Large Language Model (LLM)-based agents on edge devices. While LLM agents offer state-of-the-art capabilities, they often suffer from high end-to-end latency when deployed on resource-constrained hardware. Agent-X addresses this by optimizing both the prefill and decode stages of agent workloads without compromising accuracy. The framework utilizes two primary components: prompt rewriting to leverage prefix caching tailored to agent-specific input patterns, and LLM-free speculative decoding for rapid token generation with minimal overhead. In tests on representative agentic workloads, Agent-X achieved a 1.61x end-to-end speedup in real systems with no loss in accuracy. The authors claim this is the first study to systematically characterize and eliminate latency bottlenecks in on-device agents. The solution is designed for seamless integration into existing on-device AI architectures, marking a significant advancement in efficient edge AI deployment. This development was published as a new preprint on arXiv under the Computer Science > Artificial Intelligence category.
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