May 14, 2024, 4:44 a.m. | Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar, Uygar Kurt, Sukhbinder Singh, Faysal Ishtiaq, Cesar Mu\~noz, Prabdeep Singh Bajaj, Ali Elborady, Giann

cs.LG updates on

arXiv:2401.14109v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment. Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model …

abstract artificial artificial intelligence arxiv challenges chatgpt compression costs cs.lg deployment energy generative generative artificial intelligence inference inference costs intelligence language language models large language large language models limitations llama llms networks quant-ph quantum replace tensor training type

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