OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration
arXiv:2507.23035v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and compute resources during inference. Existing quantization methods expose a trade-off between efficiency and accuracy: weight-only quantization (WOQ) incurs costly dequantization overheads, while integer weight-and-activation quantization (INT-WAQ) reduces precision and degrades model quality. Non-uniform weight-and-activation quantization (NU-WAQ) can better capture the non-uniform distributions of LLM
The continuous growth in LLM complexity and adoption is driving an urgent need for more efficient inference, making power-efficient solutions highly sought after right now.
This development proposes a method to significantly accelerate LLM inference while maintaining accuracy, directly impacting the economic viability and scalability of AI applications.
The trade-off between LLM inference efficiency and accuracy is being directly addressed by proposed advancements in quantization methods.
- · AI hardware manufacturers
- · Cloud computing providers
- · LLM developers
- · Edge AI device makers
- · High-energy-consumption data centers
- · Companies reliant on less efficient LLM architectures
Reduced operational costs for deploying large language models becomes possible through more efficient inference.
Broader accessibility and new applications for LLMs emerge as compute constraints are eased, especially on edge devices.
The competitive landscape shifts towards companies capable of rapidly integrating and deploying power-optimized AI inference solutions.
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Read at arXiv cs.LG