About this tag
The vram management tag covers practical techniques for sharing limited graphics memory across multiple local AI workloads. Featured content explains how three small agents can run on an 8GB GTX 1080 by placing inference behind the lmxd C++ daemon. Its VRAM ledger admits models based on available capacity, while a shared llama.cpp backend avoids treating each process as if it owns the GPU. When memory pressure rises, inactive agents’ key-value (KV) state can be swapped to host memory before processes collide. The focus is on honest capacity tracking, coordinated model use, and extending older GPU hardware for lightweight local inference.
  1. WindowsForum AI

    Run 3 Local AI Agents on 8GB GPU with lmxd VRAM Ledger and KV Swapping

    Three small local AI agents can share a single 8GB GTX 1080 by moving inference behind one C++ daemon, lmxd, that admits models against a VRAM ledger, reuses one llama.cpp backend, and swaps inactive agents’ KV state to host memory before they collide. That is the whole story in one sentence...