QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy
arXiv:2606.04620v1 Announce Type: new Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks. However, they typically come at huge computational and memory costs, thus making them difficult to deploy on embedded systems. Toward this, state-of-the-art methods typically employ uniform post-training quantization (PTQ) across attention blocks of the network, hence overlooking the potential of applying different quantization levels in the same network. They also employ complex operations to mitigate the negative impact of activation outliers, hence incurring high computati
Ongoing research into optimizing Large Language Models (LLMs) for broader deployment necessitates new quantization techniques to overcome computational and memory constraints.
Efficient quantization of LLMs is critical for enabling widespread adoption on edge devices and in environments with limited resources, reducing the cost and energy footprint of AI.
This framework offers a more nuanced approach to LLM quantization, potentially improving performance on resource-constrained hardware compared to uniform methods.
- · Edge AI hardware manufacturers
- · Developers of embedded AI applications
- · Cloud providers offering quantized LLMs
- · Companies reliant solely on high-end compute for LLM deployment
- · Inefficient quantization techniques
More widespread deployment of powerful LLMs on mobile and IoT devices becomes feasible.
Reduced operational costs for AI inference could accelerate the development of AI-powered services in new sectors.
Increased accessibility of advanced AI models may democratize AI application development and foster innovation in resource-limited regions.
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Read at arXiv cs.LG