How does the inference efficiency of Qwen2.5 and Gemini 1.5 Pro scale with batch size (e.g., 1, 4, 16) on the

Abstract

Abstract: The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand for latency-insensitive tasks with batched processing, this paper initiates the study of high-throughput LLM inference using limited resources, such as a single commodity GPU. We present FlexGen, a high-throughput generation engine for running LLMs with limited GPU memory. FlexGen can be flexibly configured under various hardware resource constraints by aggregating memory and computation from the GPU, CPU, and disk. B

Research Question

How does the inference efficiency of Qwen2.5 and Gemini 1.5 Pro scale with batch size (e.g., 1, 4, 16) on the MBPP benchmark, measured in tokens-per-second and pass@k accuracy?

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