GPU Tensor Core 上大型语言模型的高效任意精度加速.pdf

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1、Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor CoresShaobo Ma,Chao Fang,Haikuo Shao,Zhongfeng WangICAIS Lab,Nanjing University,ChinaJan 23,2025OutlinesBackground&Motivation01Our WorksExperimentsConclusion02030401Background&Motivation1.1.1 Background:Quantization o

2、f LLMsChallenges Brought by the Growth in Size of LLMsMore memory(storage)More computational power and time(inference)Growth in Size of Transformer Models4BERT(340M)GPT-1(117M)GPT-2(1.5B)GPT-3(175B)GPT-4(1000+B)PaLM(540B)Gopher(280B)020040060080010001200201720182019202020212022202320241.1.1 Backgrou

3、nd:Quantization of LLMsChallenges Brought by the Growth in Size of LLMsMore memory(storage)More computational power and time(inference)One Effective MethodModel quantizationStorage requirementComputational overhead Growth in Size of Transformer Models5BERT(340M)GPT-1(117M)GPT-2(1.5B)GPT-3(175B)GPT-4

4、(1000+B)PaLM(540B)Gopher(280B)02004006008001000120020172018201920202021202220232024Challenges Brought by the Growth in Size of LLMsMore memory(storage)More computational power and time(inference)One Effective MethodModel quantizationStorage requirementComputational overhead Quantization WorksGPTQ(3-

5、4bit)1TSLD(2bit)2OneBit(1bit)31.1.1 Background:Quantization of LLMsModelsModelsFP16(GB)FP16(GB)GPTQ 3bitGPTQ 3bit(GB)(GB)TSLDTSLD(GB)(GB)OneBitOneBit(GB)(GB)LLaMA-7B13.52.51.71.3LLaMA-13B26.04.93.32.2LLaMA-30B65.112.28.14.9LLaMA-65B130.624.516.39.2Storage Reduction Brought by Model QuantizationGrowt

6、h in Size of Transformer Models61 Frantar,Elias,et al.Gptq:Accurate post-training quantization for generative pre-trained transformers.arXiv preprint arXiv:2210.17323(2022).2 Kim,Minsoo,et al.Token-scaled logit distillation for ternary weight generative language models.Advances in Neural Information