New Quantization Method Enables Single LLM Model to Run at Multiple Precision Levels
Researchers have introduced Drop-by-Drop, a post-training quantization framework that allows large language models to operate at multiple bitwidths from a single trained checkpoint without retraining. The method is grounded in information theory and uses additive codebooks with Matryoshka-style supervision to enable progressively finer weight reconstructions as more bits are added. This matters because it could significantly reduce storage and deployment costs for LLMs across hardware with varying resource constraints.
A team of researchers has proposed Drop-by-Drop, a multi-bitwidth post-training quantization (PTQ) framework designed to give LLM operators inference-time control over model precision without requiring separate retraining for each target bitwidth. The approach is theoretically motivated by information theory and successive refinement principles, leveraging the observation that LLM weights commonly follow a Gaussian distribution that can be reconstructed with increasing fidelity as additional bits are incorporated. In practice, the framework uses additive codebooks structured in a Matryoshka (nested) fashion, so that ordered subsets of codebooks yield accurate partial reconstructions at each precision level. The authors report competitive perplexity and accuracy across several major model families, including Qwen, LLaMA, Gemma, and Mistral. By consolidating multiple bitwidth configurations into a single checkpoint, Drop-by-Drop aims to reduce storage and memory overhead for organizations deploying LLMs on heterogeneous hardware. The paper spans 37 pages with 12 figures and was submitted to arXiv in June 2026.
What's missing
The paper has not yet undergone peer review, as it is a preprint. Key open questions include whether the approach generalizes to modalities beyond text. The authors' claimed 'competitive' perplexity and accuracy have not been independently validated.
What different sources said
- arXiv cs.CLCenter
Multi-Bitwidth Quantization for LLMs Using Additive Codebooks
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