Radio: Rate-Distortion Optimization for Large Language Model Compression
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908351479152640 |
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| author | Young, Sean I. |
| author_facet | Young, Sean I. |
| contents | In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM quantization from a rate-distortion theory perspective and propose a quantization technique based on simple rate-distortion optimization. Our technique scales to models containing hundreds of billions of weight parameters and offers users the flexibility to compress models, post-training, to a model size or accuracy specified by the user. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_03031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radio: Rate-Distortion Optimization for Large Language Model Compression Young, Sean I. Machine Learning Computation and Language In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM quantization from a rate-distortion theory perspective and propose a quantization technique based on simple rate-distortion optimization. Our technique scales to models containing hundreds of billions of weight parameters and offers users the flexibility to compress models, post-training, to a model size or accuracy specified by the user. |
| title | Radio: Rate-Distortion Optimization for Large Language Model Compression |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2505.03031 |