# Cephalo: Harnessing Heterogeneous GPU Clusters for Training Transformer Models

arXiv: https://arxiv.org/abs/2411.01075 · HTML: https://mlsanity.com/paper/2411.01075/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs](https://mlsanity.com/paper/2504.03871/) (2025)
- [HetCCL: Accelerating LLM Training with Heterogeneous GPUs](https://mlsanity.com/paper/2601.22585/) (2026)
- [HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training](https://mlsanity.com/paper/2609.39350/) (2026)

## Abstract (arXiv)

Training transformer models requires substantial GPU compute and memory resources. In homogeneous clusters, distributed strategies allocate resources evenly, but this approach is inefficient for heterogeneous clusters, where GPUs differ in power and memory. As high-end GPUs are costly and limited in availability, heterogeneous clusters with diverse GPU types are becoming more common. Existing methods attempt to balance compute across GPUs based on capacity but often underutilize compute due to memory constraints. We present Cephalo, a system that optimizes compute and memory usage by decoupling compute distribution from training state assignment. Cephalo outperforms state-of-the-art methods by achieving significantly higher training throughput while supporting larger models and batch sizes.