The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a ...
Abstract: Large language models (LLMs) face significant deployment challenges due to their substantial memory and computational demands. While low-precision quantization offers a promising solution, ...
OntoMem is built on the concept of Ontology Memory—structured, coherent knowledge representation for AI systems. Give your AI agent a "coherent" memory, not just "fragmented" retrieval. Traditional ...
Abstract: On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model ...
At the start of 2025, I predicted the commoditization of large language models. As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became ...
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