GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jette Oberländer, Jan Finkbeiner, Catherine M. Schöfmann, Emre Neftci
• Venue: arXiv: cs.LG
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23419v1
• PDF: https://arxiv.org/pdf/2606.23419v1
• DOI: UNKNOWN

Plain-Language Brief

The paper presents a method called GRINQH (GRaded INput-based Quantization Hierarchy) to accelerate the decoding process of language models during inference, particularly beneficial for edge-computing settings. The method unifies quantization and sparsification by dynamically assigning weight channels to different precision levels based on activation magnitudes. This results in improved performance compared to existing methods, even enabling effective 2-bit generation.

What the Document Says

• GRINQH is a post-training quantization framework for accelerating decoding in language models.
• It leverages activation magnitudes as a proxy for computational importance to dynamically assign weight channels to different precision levels.
• The method allows flexible average bit widths during decoding and outperforms state-of-the-art fixed- and mixed-precision baselines in 3- and 4-bit settings, with some success in 2-bit generation.
• GRINQH uses a hierarchical nested memory layout for multi-precision storage in a custom GPU kernel to verify theoretical speedups.
• The method establishes a new state-of-the-art Pareto frontier for LLM (Language Model) generation, allowing a dynamic trade-off between generation quality and inference speed.

Key Concepts Mentioned

• GRINQH
• Language Models (LLMs)
• Quantization
• Sparsification
• Precision Levels
• Activation Magnitudes
• GPU Memory Bandwidth

Claims Made by the Document

• GRINQH outperforms state-of-the-art fixed- and mixed-precision baselines.
• GRINQH enables effective 2-bit generation.
• GRINQH establishes a new state-of-the-art Pareto frontier for LLM generation.

Evidence Used

• Results from evaluations on Llama3 and Qwen3 models.
• Experimental verification of theoretical speedups.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Autoregressive decoding with LLMs is primarily bottlenecked by GPU memory bandwidth, especially in edge-computing settings. While quantization is essential for mitigating this bottleneck, most existing methods treat inference as a uniform process and fail to account for the asymmetry between the compute-bound prefill stage and the memory-bound decoding stage. We propose GRINQH (GRaded INput-based Quantization Hierarchy), a weight-only post-training quantization framework that accelerates decoding by unifying quantization and sparsification. GRINQH leverages activation magnitudes as a proxy for computational importance to dynamically assign weight channels to different precision levels, enabling flexible average bit widths during decoding. Evaluated on Llama3 and Qwen3 models, GRINQH outperforms state-of-the-art fixed- and mixed-precision baselines at comparable 3- and 4-bit settings, even enabling effective 2-bit generation. We experimentally verify theoretical speedups by leveraging a hierarchical nested memory layout for multi-precision storage in a custom GPU kernel. Ultimately, GRINQH establishes a new state-of-the-art Pareto frontier for LLM generation, enabling a dynamic trade-off between generation quality and inference speed.