Papers by Saleh Ashkboos
QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models (2024.emnlp-main)
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Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
| Challenge: | Large Language Models (LLMs) are extremely popular, leading to a race towards reducing their inference costs. |
| Approach: | They propose a method that quantizes weights and activations to 4 bits to achieve better accuracy. |
| Outcome: | The proposed method reduces runtime costs in memory-bound models but does not address cost-bound scenarios. |