arXiv:2605.13768v2 Announce Type: replace Abstract: This is the second part of the work investigating quantized matrix multiplication (MatMul). In part I we considered the case of calibration-free quantization, whereas here we discuss the setting where covariance matrix $\Sigma_X$ of the columns of the second factor is available. This setting arises in the ubiquitous task of weight-only post-training quantization of LLMs. Weight-only quantization is related to the problem of weighted mean squared error (WMSE) source coding, whose classical (reverse) waterfilling solution dictates how one shoul
This paper represents a continuation of advanced research into optimizing quantized matrix multiplication, a critical bottleneck in the efficiency of large language models, indicating ongoing, rapid innovation.
Improved quantization techniques directly enhance the performance and reduce the computational cost of AI models, making them more accessible and deployable.
New methods for high-rate quantized matrix multiplication, particularly in 'weight-only' post-training quantization, will lead to more efficient and powerful AI hardware and software.
- · AI hardware manufacturers
- · Cloud AI providers
- · Large Language Model developers
- · Edge AI computing
- · Companies reliant on inefficient AI compute
Further optimization of LLMs, reducing their memory footprint and energy consumption.
Accelerated deployment of advanced AI applications on resource-constrained devices, such as mobile or edge hardware.
Increased competition and innovation in AI model development due to lower barriers to entry for training and inference.
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Read at arXiv cs.LG