Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models

• URL: http://arxiv.org/abs/2506.23025v1
• Date: Sat, 28 Jun 2025 22:13:43 GMT
• Title: Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
• Authors: Tejas Vaidhya, Ayush Kaushal, Vineet Jain, Francis Couture Harpin, Prashant Shishodia, Majid Behbahani, Yuriy Nevmyvaka, Irina Rish,
• Abstract summary: Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge.<n>We investigate ternary language models (TriLMs) that employ quantization-aware training to significantly reduce memory requirements.<n>We introduce Spectra-1.1, an open suite of TriLMs trained on up to 1.2 trillion tokens, demonstrating sustained performance gains at scale.
• Score: 12.98064202867316
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge. As the computational power of modern GPU architectures continuously improves, their memory bandwidth and capacity have not scaled proportionally, creating a critical bottleneck during inference. To address this, we investigate ternary language models (TriLMs) that employ quantization-aware training to significantly reduce memory requirements. We first analyze the scalability of TriLMs by conducting a scaling law analysis, revealing that TriLMs benefit more from increasing training data than from scaling model parameters. Based on this observation, we introduce Spectra-1.1, an open suite of TriLMs trained on up to 1.2 trillion tokens, demonstrating sustained performance gains at scale. Furthermore, to improve inference efficiency, we propose novel 2-bit and 1.6-bit packing schemes for ternary weights, which demonstrate accelerated inference across various CPU architectures. Also, building on the 2-bit packing, we develop a GPU kernel called TriRun that accelerates end-to-end model inference by up to 5 times compared to floating-point baselines. To encourage further exploration and development of TriLMs, we will release the Spectra-1.1 suite and TriRun inference kernels. Overall, our work lays the foundation for building and deploying efficient LLMs, providing a valuable resource for the research community.

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