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                    "Title:Breaking the 1.58-bit Barrier for Ternary LLMs\n                \n    View PDF\n    HTML (experimental)\n            Abstract:Ternary Large Language Models (LLM) store every weight as one of three symbols $\\{-1,0,+1\\}$, so the cost of a ternary model is conventionally referenced to the information-theoretic $\\log_2 3 \\approx 1.585$ bits per weight. The prevailing deployment format packs five ternary weights into one byte (five-trit packing), and due to the power-of-two group sizes used in practice this rounds up to $1.625$ bits per weight. This effective storage bit-width treats the three symbols $\\{-1,0,+1\\}$ as equiprobable. We measure the actual symbol distribution of 29 ternary LLM models and find that zeros account for up to $51.5\\%$ of all weights. Motivated by this finding, we introduce BITCOS, a simple distribution-adaptive layout comprised of a dense presence bitmap plus a compacted sign vector, and costs $2 - z$ bits per weight element given a zero density $z$ in the model's weights. BITCOS stores weights more compactly than the five-trit packing in 26 of the 29 tested models, and reaches $1.485$ bits per weight on the sparsest of them. BITCOS is amenable to efficient unpacking on modern processors and GPUs, and we present optimized unpacking sequences for AVX-512, AVX2 and Intel Xe2 GPUs. Measured against production state-of-the-art ternary matrix-vector multiplication kernels, at the zero densities real-world ternary models exhibit, the realized gain with our proposed layout is up to $1.28\\times$. Finally, we illustrate end-to-end LLM inference results on 5 different platforms (client and server CPUs, integrated and discrete Xe2 GPUs) where decode throughput improves by up to $1.18\\times$ on CPUs and $1.27\\times$ on GPUs.\n    \n\n    \n    \n  \n      Submission history From: Evangelos Georganas [view email]          [v1]\n        Mon, 14 Sep 2026 20:54:24 UTC (144 KB)"
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