arXiv:2505.17595v4 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPUs or personal devices such as laptops, due to high memory consumption and inference costs. Post-training quantization (PTQ) of LLMs offers a promising solution that reduces their memory footprint and decoding latency. In practice, PTQ with uniform quantization representation is favored due to its efficiency and ease of deployment, as uniform quantization is widely supported by mainstream hardware and so
The proliferation of LLMs creates an urgent need for efficient deployment on consumer hardware, making quantization research highly relevant.
This development makes powerful LLM capabilities more accessible and reduces the computational burden, broadening their potential application in edge devices.
Local deployment of advanced LLMs becomes more feasible and cost-effective for end-users, reducing reliance on cloud-based inference.
- · Device manufacturers
- · Consumers
- · Edge AI developers
- · AI hardware startups
- · Companies reliant solely on large-scale cloud inference for LLMs
Reduced computational and memory requirements for running large language models locally.
Increased adoption and integration of LLMs into consumer-grade devices and personal computing.
Enhanced data privacy and reduced latency for AI applications due to more on-device processing.
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