A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM
PrismLLM is a new framework designed to emulate large language model training using only a few GPUs. This approach allows engineers to replicate large-scale behaviors without needing extensive access to production clusters. Experiments have shown that PrismLLM can accurately reproduce performance metrics with minimal error rates.
- ▪PrismLLM enables the emulation of large-scale LLM training using less than 1% of the physical GPUs required.
- ▪It constructs a high-fidelity execution graph to capture computation, communication, and dependencies.
- ▪The framework achieved an average error of only 0.58% in iteration time and less than 0.01% in peak GPU memory usage.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15617 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.15617 (cs) [Submitted on 15 May 2026] Title:A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM Authors:Shaoke Xi, ChonLam Lao, Boyi Jia, Jiaqi Gao, Zhipeng Zhang, Jiamin Cao, Brian Sutioso, Erci Xu, Minlan Yu, Kui Ren, Yong Li, Zhengping Qian, Ennan Zhai, Jingren Zhou View a PDF of the paper titled A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM, by Shaoke Xi and 13 other authors View PDF Abstract:Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debugging, and performance-tuning the training framework inevitably becomes complex and costly.
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