arXiv:2606.12876v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the trade-off between performance and efficiency without retraining is critical. We propose Drop-by-Drop, a novel multi-bitwidth post-training quantization framework that enables inference-time precision control over LLM weights from a single trained model. Our method is theoretically grounded in information theory and successive refinement. We establish that LLM weights, which commonly foll
The proliferation of LLMs across diverse hardware environments increasingly necessitates efficient resource management, prompting innovation in post-training quantization techniques.
This development allows LLMs to run more efficiently on various devices without retraining, enabling broader deployment and reducing computational overhead for AI-driven applications.
A single LLM can now dynamically adjust its precision based on hardware constraints at inference time, optimizing performance and resource use without needing multiple models.
- · AI hardware manufacturers (edge devices)
- · Cloud providers (cost savings)
- · LLM developers (broader deployment)
- · Mobile computing
- · Developers reliant on high-precision-only models
- · Companies offering only monolithic, high-resource LLMs
LLMs become more accessible and cost-effective to deploy on resource-constrained hardware.
Increased adoption of sophisticated AI in edge computing and mobile applications, fostering new use cases.
Potentially shifts market share towards companies optimizing for efficient, flexible model deployment rather than raw computational power alone.
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