Computer Science > Machine Learning
arXiv:2311.00502 (cs)
[Submitted on 1 Nov 2023 (v1), last revised 7 Dec 2023 (this version, v2)]
Title:Efficient LLM Inference on CPUs
View a PDF of the paper titled Efficient LLM Inference on CPUs, by Haihao Shen and 4 other authors
View PDFHTML (experimental)Abstract:Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the astronomical amount of model parameters, which requires a demand for large memory capacity and high memory bandwidth. In this paper, we propose an effective approach that can make the deployment of LLMs more efficiently. We support an automatic INT4 weight-only quantization flow and design a special LLM runtime with highly-optimized kernels to accelerate the LLM inference on CPUs. We demonstrate the general applicability of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase the extreme inference efficiency on CPUs. The code is publicly available at:this https URL.
| Comments: | NeurIPS'2023 on Efficient Natural Language and Speech Processing |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2311.00502 [cs.LG] |
| (orarXiv:2311.00502v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2311.00502 arXiv-issued DOI via DataCite |
Submission history
From: Haihao Shen [view email][v1] Wed, 1 Nov 2023 13:08:50 UTC (133 KB)
[v2] Thu, 7 Dec 2023 12:16:42 UTC (133 KB)
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View a PDF of the paper titled Efficient LLM Inference on CPUs, by Haihao Shen and 4 other authors
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