George Pallis

MCP-GRANITE is presented, an open-source extensible benchmark framework that treats tool-interface granularity as a controlled variable for MCP-based agents, evaluated under edge and IoT scenarios and identifies tool-interface granularity as a key design parameter for MCP-based agents.

A controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes, highlighting that compute-side inference metrics alone can lead to suboptimal placement for latency-sensitive interactive web services.

Sustainability-optimal data center choices can significantly differ from latency-optimal ones, revealing the hidden costs of ignoring local grid carbon intensity.