arXiv:2512.00956v3 Announce Type: replace Abstract: Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization errors. Prior transform-based mitigations (e.g., Hadamard rotations) are fixed and data-agnostic, and their optimality for quantization has remained unclear. We derive closed-form optimal linear blockwise transforms for joint weight-activation quantization under standard RTN AbsMax-scaled block quantizers, covering both integer and floating-point formats. The resultin
The increasing scale of LLMs and the demand for their efficient deployment across various hardware necessitate more effective quantization techniques to reduce computational load and memory footprint.
This development allows for more accurate and efficient deployment of large language models, broadening their practical applications and reducing the cost barrier for advanced AI capabilities.
The previous heuristic and fixed transform methods for LLM quantization are replaced by a near-optimal, adaptive approach that significantly improves efficiency without compromising performance.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Businesses adopting LLMs
- · Companies reliant on inefficient LLM deployment
- · Current fixed quantization methods
More widespread and cost-effective deployment of advanced LLMs.
Accelerated innovation in AI applications due to lower inference costs and increased accessibility.
Even smaller, more power-constrained devices will be able to run increasingly sophisticated AI models, broadening the scope of AI integration into daily life and specialized systems.
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Read at arXiv cs.LG