Splitwiser: Efficient LM inference with constrained resources

• URL: http://arxiv.org/abs/2505.03763v1
• Date: Mon, 21 Apr 2025 00:21:08 GMT
• Title: Splitwiser: Efficient LM inference with constrained resources
• Authors: Asad Aali, Adney Cardoza, Melissa Capo,
• Abstract summary: Splitwiser is a methodology that splits the two phases of an LLM inference request onto the same GPU.<n>By eliminating the need to transfer data across devices, Splitwiser aims to minimize network-related overheads.<n>We implement our proposed multiprocessing design on two widely-used and independent LLM architectures: Huggingface and vLLM.
• Score: 0.29260385019352086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient inference of LLMs remains a crucial challenge, with two main phases: a compute-intensive prompt computation and a memory-intensive token generation. Despite existing batching and scheduling techniques, token generation phases fail to fully utilize compute resources, especially when compared to prompt computation phases. To address these challenges, we propose Splitwiser, a methodology that splits the two phases of an LLM inference request onto the same GPU, thereby reducing overhead and improving memory access and cache utilization. By eliminating the need to transfer data across devices, Splitwiser aims to minimize network-related overheads. In this report, we describe the basic structure of our proposed pipeline while sharing preliminary results and analysis. We implement our proposed multiprocessing design on two widely-used and independent LLM architectures: Huggingface and vLLM. We open-source our code for the respective implementations: 1) Huggingface (https://github.com/asad-aali/splitwiser), and 2) vLLM (https://github.com/adney11/vllm-sysml).

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