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importantSYS.SOURCE: arXiv• 2026-09-16T20:59:24Z

Advancing Ternary Large Language Models: Efficient Bit-Width Optimization

#ai#machine-learning#neural-networks#optimization#hardware

The paper introduces BITCOS, a distribution-adaptive storage format for ternary LLMs that reduces bit-width requirements below the 1.58-bit theoretical minimum by exploiting zero density, achieving 1.485 bits per weight and up to 1.28× performance gains in matrix operations.

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