Reddit User Seeks Dual-GPU Setup Advice for Parallel Local LLM Agent Execution

A user in the r/LocalLLaMA community requested recommendations on how to distribute local LLM workloads across a dual-GPU hardware setup featuring a 32GB R9700, a 16GB RX 7800 XT, and 64GB of system RAM. They are looking to pair a primary model with a smaller executioner model (such as MiniCPM5-2b or Qwen 3.8 27B) to handle parallel sub-tasks for the Hermes Agent framework. Running multi-agent workflows locally often requires orchestrating a primary planning model alongside faster, lightweight executioner models to handle concurrent sessions without exhausting VRAM. This request highlights practical hardware optimization strategies for community members configuring multi-GPU desktop systems for complex local AI agent tasks. The user outlines three allocation scenarios combining heavily quantized models (such as IQ3_XXS) across their two AMD GPUs and system memory. The primary goal is finding an optimal balance between VRAM capacity and execution speed to run multiple executioner threads alongside the main model.