# Computational Elasticity: Quantifying the Relationship Between Inference Accuracy of Large Language Models and Computational Resources During Inference

> This article introduces a pre-registered pilot study that systematically analyzes how the inference accuracy of large language models on the GPQA Diamond benchmark scales with the increase of computational resources during inference using a parametric curve fitting method.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-23T13:44:01.000Z
- 最近活动: 2026-05-23T13:48:47.854Z
- 热度: 0.0
- 关键词: 大语言模型, LLM, 推理时计算, scaling law, GPQA, 计算弹性, 参数拟合, 推理精度, 人工智能, 机器学习
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-u7k4rs6-compute-elasticity
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-u7k4rs6-compute-elasticity
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Computational Elasticity: Quantifying the Relationship Between Inference Accuracy of Large Language Models and Computational Resources During Inference

This article introduces a pre-registered pilot study that systematically analyzes how the inference accuracy of large language models on the GPQA Diamond benchmark scales with the increase of computational resources during inference using a parametric curve fitting method.