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MOTHRAG is a multi-hop RAG system that matches the performance of top GPU-dependent systems (HippoRAG 2, CoRAG, NeocorRAG) using only commodity API calls, with no GPU, no fine-tuning, and deployment via pip install plus API keys.
A quiet revolution is making powerful AI models runnable on consumer hardware without expensive GPUs, thanks to breakthroughs in quantization and optimized implementations like llama.cpp's Gemma4 MTP support, democratizing access for hobbyists, small businesses, and edge computing.
The author proposes two architectures, Internal KV-Sphere Architecture (IKSA) and Background Micro Fine-Tuning (BMFT), for enabling LLMs to learn continually from personal interactions without GPU requirements and without catastrophic forgetting.