RESEARCH · RESEARCH · #1316
AgBench: benchmark suite for agentic AI on personal devices (arXiv:2609.38652v1)
AgBench is a benchmark suite and open artifacts for reproducible evaluation of agentic AI on personal devices, comparing local, hybrid, and cloud execution across workloads. Based on over 162.07 million data points, the authors report that local-only execution removes cloud API costs and sensitive-data exposure but tends to have lower task success and longer completion times than cloud-only execution; hybrid approaches can improve success but trade off cost and exposure depending on how work is divided.
KEY POINTS
- AgBench is a benchmark suite and open artifacts for reproducible evaluation of agentic AI on personal devices, comparing local, hybrid, and cloud execution across workloads.
- Based on over 162.07 million data points, the authors report that local-only execution removes cloud API costs and sensitive-data exposure but tends to have lower task success and longer completion times than cloud-only execution; hybrid approaches can improve success but trade off cost and exposure depending on how work is divided.
- AgBench provides a systematic, reproducible framework to quantify the cost, privacy, latency, and success trade-offs of deploying agentic AI locally versus in the cloud, informing device and deployment design choices.
WHY IT MATTERS
AgBench provides a systematic, reproducible framework to quantify the cost, privacy, latency, and success trade-offs of deploying agentic AI locally versus in the cloud, informing device and deployment design choices.