Year: 2024
Type: conference-paper
Abstract: When trained Large Language Models (LLMs) become available, it is desirable to carry out LLM inferences at the user end with limited resources. A common belief on LLM inference is that GPU is essentially the only meaningful processor as almost all computation is tensor multiplication that GPU excels in. However, this belief and its practice are challenged by the fact that GPU has insufficient memory and runs at a much slower speed due to constantly waiting for data to be loaded from the CPU memory via a slow PCIe bus. This makes the CPU a processor with meaningful computing power that can be leveraged to accelerate the inference.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402805837