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A Survey of Resource-efficient LLM and Multimodal Foundation Models

Paper · 2024 · Mengwei Xu, Wangsong Yin, Dongqi Cai et al.

Summary

Large foundation models such as LLMs, vision transformers, diffusion models and LLM-based multimodal systems demand enormous compute, memory, energy and bandwidth. These costs limit sustainability and keep cutting-edge models in the hands of a few companies. This survey, written by authors from BUPT, Peking University and Tsinghua, reviews research that reduces these costs without hurting quality. It is a literature review and introduces no new dataset or model. The authors arrange the field into three layers. The first covers efficient architectures, including attention variants, MoE, early exiting and optimizations specific to diffusion models and ViTs. The second covers algorithms for pre-training, fine-tuning, inference and compression. The third covers systems for distributed and federated training and for serving on cloud and edge devices. The review concentrates on algorithm and system work published since 2020 and leaves hardware design aside. It closes with open research problems. For teams with limited budgets, the survey is a practical guide to methods such as PEFT, quantization, KV-cache handling and on-device deployment.

Key points

  • Cost examples: Pre-training LLaMa-2-70B took about 1.7 million GPU hours and roughly 2.5×10¹² J of energy, with an estimated 291 t CO2e. One projection has NVIDIA shipping 1.5 million AI servers a year by 2027, which would draw at least 85.4 TWh annually. That exceeds the yearly electricity use of New Zealand or Austria.
  • Scope: "Resources" here means physical ones such as compute, memory, storage and bandwidth, not labels or privacy. Sources are mainly CSRankings-listed venues plus selected arXiv preprints.
  • Fine-tuning and compression: The survey groups fine-tuning methods into additive, selective and re-parameter tuning. Compression is split into pruning, knowledge distillation, quantization and low-rank decomposition.
  • On-device deployment: Edge work covered includes mllm-NPU, which speeds up LLM prefill on mobile NPUs. It also discusses the LLM-as-a-Service idea, with examples such as Google's Android AICore, LMS and ELMS.
  • Future directions: The authors name six open areas:
    • cloud-edge hybrid deployment
    • exploiting activation sparsity
    • foundation models offered as a service
    • optimizing agents as whole systems
    • practical privacy-preserving foundation models
    • better understanding of the scaling law
  • Open materials: All survey materials are available at github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

Cite

Cite as Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin and Xuanzhe Liu. 2024. A Survey of Resource-efficient LLM and Multimodal Foundation Models. arXiv:2401.08092.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2401.08092 · arXiv 2401.08092

@misc{xu2024survey,
  title = {{A Survey of Resource-efficient LLM and Multimodal Foundation Models}},
  author = {Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin and Xuanzhe Liu},
  year = {2024},
  doi = {10.48550/arxiv.2401.08092},
  eprint = {2401.08092},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2401.08092}
}

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