TransArch: Hardware-Aware Architecture Transfer for Foundation Models

Modern large language models are increasingly bottlenecked by communication rather than computation on today's hardware. TransArch is a collection of research projects that design hardware-friendly model architectures and migrate existing pre-trained models into these new architectures with minimal performance loss.

📦 Projects

📰 News

• [2026.05] GQLA code released: see GQLA_preprint/.
• [2026.05] GQLA preprint released: arXiv:2605.15250.
• [2026.05] MISA preprint released: arXiv:2605.07363.
• [2026.03] HISA preprint released: arXiv:2603.28458.
• [2026.02] 🎉 TransMLA is adopted by Ant Group's latest 1T model Ling-2.5-1T! This demonstrates the robust scalability of TransMLA in ultra-large-scale LLMs.
• [2025.11] TPLA accepted at ASPLOS 2026 (Summer cycle).
• [2025.09] TransMLA accepted at NeurIPS 2025 (Spotlight, Top 3.19%).
• [2025.05] CLOVER accepted at ICML 2025.

📋 To-Do

• [ ] Release TPLA code
• [ ] Release HISA code
• [ ] Release MISA code

📚 Citation

If you find our work useful, please cite the relevant paper(s):

@inproceedings{meng2025transmla,
  title={TransMLA: Multi-head Latent Attention Is All You Need},
  author={Meng, Fanxu and Tang, Pingzhi and Tang, Xiaojuan and Yao, Zengwei and Sun, Xing and Zhang, Muhan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025}
}

@inproceedings{meng2025clover,
  title={CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning},
  author={Meng, Fanxu and Tang, Pingzhi and Jiang, Fan and Zhang, Muhan},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2025}
}

@inproceedings{tang2026tpla,
  title={TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference},
  author={Tang, Xiaojuan and Meng, Fanxu and Tang, Pingzhi and Wang, Yuxuan and Yin, Di and Sun, Xing and Zhang, Muhan},
  booktitle={International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
  year={2026}
}

@article{xu2026hisa,
  title={HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention},
  author={Xu, Yufei and Meng, Fanxu and Jiang, Fan and Wang, Yuxuan and Zhou, Ruijie and Wang, Zhaohui and Wu, Jiexi and Pan, Zhixin and Tang, Xiaojuan and Pei, Wenjie and Liu, Tongxuan and Yin, Di and Sun, Xing and Zhang, Muhan},
  journal={arXiv preprint arXiv:2603.28458},
  year={2026}
}

@article{zhou2026misa,
  title={MISA: Mixture of Indexer Sparse Attention for Long-Context LLM Inference},
  author={Zhou, Ruijie and Meng, Fanxu and Xu, Yufei and Liu, Tongxuan and Lu, Guangming and Zhang, Muhan and Pei, Wenjie},
  journal={arXiv preprint arXiv:2605.07363},
  year={2026}
}

@article{meng2026gqla,
  title={GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding},
  author={Meng, Fanxu},
  journal={arXiv preprint arXiv:2605.15250},
  year={2026}
}

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