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ARXIV:2603.16590 · QUANTIZATION TECHNIQUES · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16590QUANTIZATION TECHNIQUESSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact.
Opportunity summary
Pain BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when…
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on both MLLMs and LLMs demonstrate that BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations, recovering up to 96.43% of full-precision…
Quantization Techniques moved forward this cycle; last verified April 2026. Public score 8.0/10.
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BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact.
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10.48550/arXiv.2603.16590BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact.
Abstract
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4. Recent studies attribute this failure to a fundamental format mismatch: global orthogonal rotations inadvertently transfer outlier energy across quantization blocks, inducing new outliers that disrupt local block-wise scaling, while often creating bimodal activation distributions that underutilize the limited quantization range. To address these issues, we propose BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation, while relaxing orthogonality constraints to optimize distribution shaping. To ensure parameter efficiency, we introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead and incorporate Block-wise Learnable Clipping to suppress residual outliers. Extensive experiments on both MLLMs and LLMs demonstrate that BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations, recovering up to 96.43% of full-precision performance on multimodal benchmarks and clearly outperforming existing methods across diverse tasks.
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Dimensions overall score 8.0
PROBLEM
BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer...
METHOD
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on both MLLMs and LLMs demonstrate that BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations, recovering up to 96.43% of full-precision performa...
WHY NOW
Quantization Techniques moved forward this cycle; last verified April 2026. Public score 8.0/10.
BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations
The abstract explicitly states 'BATQuant establishes new state-of-the-art results under aggressive W4A4KV16 configurations'.
partial
recovering up to 96.43% of full-precision performance on multimodal benchmarks
The abstract provides a specific performance recovery percentage: 'recovering up to 96.43% of full-precision performance on multimodal benchmarks'.
partial
existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4.
The abstract clearly states the problem with existing methods: 'existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4.'
partial
restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation
The abstract describes a core aspect of BATQuant's methodology: 'BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation'.
partial
incorporate Block-wise Learnable Clipping to suppress residual outliers.
The abstract details another component of the BATQuant method: 'incorporate Block-wise Learnable Clipping to suppress residual outliers.'
partial
introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead
The abstract mentions the GPK decomposition as a feature for efficiency: 'we introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead'.
partial
clearly outperforming existing methods across diverse tasks.
The abstract provides a comparative statement about performance: 'clearly outperforming existing methods across diverse tasks.'
partial
To address these issues, we propose BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation
The abstract explains the motivation for BATQuant by referencing the 'fundamental format mismatch' and how BATQuant addresses it.
partial
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BATQuant optimizes quantization for multi-modal large language models, achieving state-of-the-art performance while minimizing outlier impact.
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