A comprehensive tutorial on running multiple local LLMs simultaneously on a single GPU. Covers memory budgeting via nvidia-smi/system_profiler, model quantization tradeoffs (FP16 vs Q8 vs Q4), LRU-based dynamic model loading/unloading, GPU-CPU layer offloading with llama.cpp, and memory-aware request queuing. Includes full Node.js/Express orchestration code with a model registry, ModelManager class, and a React dashboard that polls a /health endpoint every 2 seconds to visualize VRAM usage and loaded models in real time.
Table of contents
Table of ContentsPrerequisitesWhy Multi-Model Setups Are the New DefaultUnderstanding Memory Constraints When Running Multiple Local ModelsCore Memory Management StrategiesBuilding the Orchestration Layer with Node.jsBuilding a Real-Time Monitoring Dashboard with ReactPerformance Tuning and TroubleshootingComplete Implementation ChecklistScaling Locally Without Scaling HardwareSort: