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AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLMQuantizationAnyprecision
Abstract:

The deployment of large language models (LLMs) is increasingly constrained by memory and latency bottlenecks, motivating the need for quantization techniques that flexibly balance accuracy and efficiency. Recent work has introduced multi-precision models, which enable inference at multiple precisions within a single model depending on runtime constraints. To support such flexibility, quantized weights are often stored as bit-planes, where hardware efficiency improves when the compute operates directly at the bit-plane level and activates only the precision required by each request. In this work, we present AnyBCQ, a hardware-friendly multi-precision extension of Binary-Coded Quantization (BCQ) that supports direct bit-plane operations. By representing weights as binary bit-planes with corresponding scale factors, AnyBCQ enables bit-plane–level computation and maps naturally to accelerator-friendly, bit-parallel arithmetic. Our progressive precision expansion mechanism incrementally refines scaling factors while reusing previously assigned binary codes, yielding monotonic improvements in accuracy as additional bits are enabled. We further co-design a specialized kernel that exploits the BCQ structure to support dynamic per-request precision selection with negligible overhead. Experiments on recent LLMs demonstrate that AnyBCQ significantly narrows the accuracy drop in the low-bit regime (e.g. 2-bit), remains competitive at higher precision, and achieves throughput gains of up to 3.0×3.0\times over half precision and 1.2×1.2\times over state-of-the-art multi-precision methods. By aligning algorithmic flexibility with hardware efficiency, AnyBCQ provides a practical foundation for multi-precision LLM deployment across diverse service-level objectives.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces AnyBCQ, a multi-precision quantization framework that represents weights as binary bit-planes with corresponding scale factors, enabling direct bit-plane computation. It resides in the 'Binary-Coded and Bit-Plane Quantization' leaf under 'Novel Data Formats and Encoding Schemes'. Notably, this leaf contains only the current paper among the fifty papers in the taxonomy, indicating a sparse research direction. The framework targets hardware-friendly deployment by mapping naturally to bit-parallel arithmetic, distinguishing it from conventional fixed-point or floating-point quantization schemes.

The taxonomy reveals that neighboring leaves focus on alternative encoding strategies: 'Group-Shared Exponent and Block Formats' explores shared exponents across weight groups, while sibling branches address 'Mixed-Precision Allocation Strategies' (assigning different bit-widths per layer or channel) and 'Hardware-Software Co-Design' (optimizing for specific accelerators). AnyBCQ diverges by proposing a fundamentally different numerical representation—bit-plane decomposition—rather than optimizing allocation policies or hardware mappings for standard formats. This positions the work at the intersection of encoding innovation and hardware efficiency, bridging format design with accelerator-friendly computation.

Among twenty-three candidates examined via semantic search and citation expansion, no papers were found to clearly refute any of the three core contributions. The 'AnyBCQ multi-precision quantization framework' examined ten candidates with zero refutable overlaps; the 'progressive precision expansion mechanism' examined three candidates, also with zero refutations; and the 'hardware-efficient CUDA kernel for bit-plane operations' examined ten candidates, again with zero refutations. This suggests that within the limited search scope, the combination of binary-coded representation, progressive scaling refinement, and specialized kernel design appears relatively unexplored, though the small candidate pool precludes definitive claims about the broader literature.

Given the sparse taxonomy leaf and absence of refuting prior work among the examined candidates, the contributions appear novel within the surveyed scope. However, the analysis is constrained by the top-K semantic search methodology and does not constitute an exhaustive review. The framework's distinctiveness lies in its encoding-centric approach to multi-precision quantization, which may occupy a niche between established allocation strategies and extreme low-bit methods, though broader validation would require examining additional hardware-oriented quantization literature beyond the current search radius.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: multi-precision quantization for large language models. The field has evolved into a rich landscape of techniques that balance memory efficiency, computational cost, and model accuracy. At the highest level, the taxonomy reveals several major branches: Mixed-Precision Allocation Strategies focus on assigning different bit-widths to layers or channels based on sensitivity metrics (e.g., LLM-MQ[10], FGMP[27]); Low-Rank and Residual-Based Quantization methods decompose weights to isolate hard-to-quantize components (e.g., LoftQ[1], ResQ[9]); KV Cache Quantization targets memory bottlenecks during inference (e.g., KVTuner[28]); Extreme Low-Bit and Sparse Quantization pushes toward binary or ternary representations (e.g., BitNet[15], Onebit[7]); Dynamic and Runtime-Adaptive Quantization adjusts precision on-the-fly; Hardware-Software Co-Design explores accelerator-friendly formats; Novel Data Formats and Encoding Schemes introduce alternative numerical representations; Quantization Error Mitigation techniques reconstruct or compensate for quantization loss; Theoretical Analysis examines scaling laws; and Comprehensive Frameworks provide unified benchmarks and toolkits. Within this ecosystem, a particularly active line of work explores novel encoding schemes that depart from standard fixed-point representations. AnyBCQ[0] sits squarely in the Binary-Coded and Bit-Plane Quantization cluster under Novel Data Formats, proposing flexible bit-plane decompositions that enable fine-grained control over precision without traditional rounding. This contrasts with mixed-precision allocation methods like Channel-Wise Mixed-Precision[2] or MixQ[13], which assign conventional bit-widths per layer or channel, and with low-rank approaches like LoftQ[1] that factor out residuals rather than rethinking the encoding itself. The central trade-off across these branches is between the simplicity of uniform quantization, the adaptability of mixed-precision search, and the potential efficiency gains from custom data formats. AnyBCQ[0] emphasizes the latter, offering a middle ground where bit-plane coding provides structured flexibility, distinguishing it from both coarse-grained allocation strategies and extreme low-bit methods that sacrifice representational richness for minimal storage.

Claimed Contributions

AnyBCQ multi-precision quantization framework

The authors introduce AnyBCQ, a quantization framework that extends Binary-Coded Quantization to support multiple precision levels within a single model. It uses progressive precision expansion to incrementally refine scaling factors while reusing binary codes, enabling monotonic accuracy improvements as additional bits are activated.

10 retrieved papers
Progressive precision expansion mechanism

The authors develop a mechanism that starts from a base-precision quantized model and progressively expands to higher precisions by freezing existing binary codes and adding residual bit-planes with new scaling factors. This approach ensures smooth accuracy improvements across precision levels while sharing binary representations to minimize memory overhead.

3 retrieved papers
Hardware-efficient CUDA kernel for bit-plane operations

The authors design a CUDA kernel that operates directly on binary bit-planes without requiring bit transposition or centroid table lookups. This kernel enables dynamic precision selection at runtime by fetching only the required bit-planes and performing efficient bit-parallel arithmetic, achieving throughput gains over existing methods.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AnyBCQ multi-precision quantization framework

The authors introduce AnyBCQ, a quantization framework that extends Binary-Coded Quantization to support multiple precision levels within a single model. It uses progressive precision expansion to incrementally refine scaling factors while reusing binary codes, enabling monotonic accuracy improvements as additional bits are activated.

Contribution

Progressive precision expansion mechanism

The authors develop a mechanism that starts from a base-precision quantized model and progressively expands to higher precisions by freezing existing binary codes and adding residual bit-planes with new scaling factors. This approach ensures smooth accuracy improvements across precision levels while sharing binary representations to minimize memory overhead.

Contribution

Hardware-efficient CUDA kernel for bit-plane operations

The authors design a CUDA kernel that operates directly on binary bit-planes without requiring bit transposition or centroid table lookups. This kernel enables dynamic precision selection at runtime by fetching only the required bit-planes and performing efficient bit-parallel arithmetic, achieving throughput gains over existing methods.

Table of Contents

AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs | Novelty Validation