Efficiently Serving Large Language Models On Edge Devices With TPI-LLM
Researchers propose TPI-LLM to run large language models on low-resource edge devices, splitting the model across multiple devices & reducing memory footprint, achieving comparable performance with lower resource requirements.
This is a Plain English Papers summary of a research paper called AI unlocking huge language models for tiny edge devices. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview The paper proposes a novel technique called TPI-LLM to efficiently serve large language models (LLMs) with up to 70 billion parameters on low-resource edge devices. TPI-LLM leverages tensor partitioning and pipelining to split the model across multiple devices, enabling parallel processing and reducing the memory footprint. Experimental results show that TPI-LLM ca...