ScaleSweep: Accurate NVFP4 Post-Training Quantization of LLMs via Block Scale Initialization

ScaleSweep introduces an optimized block scale initialization method for NVFP4 quantization of large language models, improving upon traditional AbsMax approaches. The technique theoretically bounds the search space and empirically achieves 93% performance retention under aggressive 4-bit quantization, advancing hardware-efficient AI inference.

ScaleSweep addresses a fundamental challenge in making large language models deployable on edge devices and resource-constrained hardware. As LLMs scale exponentially in size, quantization—converting high-precision weights to lower-precision formats—becomes essential for practical deployment. NVFP4 represents a hardware vendor's attempt to standardize 4-bit quantization with fine-grained scaling, but existing initialization methods fail to exploit this architecture fully. The research demonstrates that systematic scale optimization substantially outperforms simpler heuristics, with theoretical bounds making the approach computationally tractable. This matters because quantization quality directly impacts model accuracy; preserving 93% of full-precision performance under aggressive end-to-end quantization (weights, activations, KV cache, and query states) is a meaningful engineering achievement. The work builds on the broader trend of post-training quantization becoming the dominant path to efficiency, rather than quantization-aware training. Industry players deploying LLMs in production face trade-offs between latency, memory, and accuracy—ScaleSweep tilts this balance toward accuracy without significant computational overhead. For developers, this enables previously infeasible deployments on mobile and embedded systems. The validation on Llama and Qwen models—representing both open and commercial ecosystems—suggests broad applicability. Looking ahead, the impact depends on NVFP4 hardware adoption across inference accelerators and whether competing quantization formats emerge with superior trade-offs.

• →ScaleSweep achieves 93% full-precision performance under aggressive 4-bit quantization across weights, activations, KV cache, and query states
• →Theoretical bounds reduce the quantization search space while maintaining optimal solutions, minimizing computational overhead
• →Method outperforms existing AbsMax initialization approaches consistently across Llama and Qwen model families
• →NVFP4 block-scale quantization represents industry movement toward standardized hardware-supported inference optimization
• →Post-training quantization advances enable practical LLM deployment on resource-constrained edge and mobile devices