The massive size of Large Language Models (LLMs) imposes substantial computational and storage burdens, particularly on devices with limited hardware resources. Compared to foundation models, smaller and more specialized models are often more suitable for practical deployment. Existing customization approaches, such as the conventional “prune-then-finetune” paradigm or task-agnostic deployment strategies, either incur excessive computational costs or lead to suboptimal task performance. The recently popular Mixture-of-Experts (MoE) architecture exhibits a strong ability to mitigate inter-task interference, offering a new perspective on model deployment. In this paper, we introduce ModularMoE, a training framework that converts pre-trained LLMs into parameter-sharing MoE models for lightweight deployment. Exploiting the emergent modularity within LLMs, we split the feed-forward layers into multiple disjoint modules. Each expert is then constructed as a combination of such modules, enabling knowledge sharing across experts and thereby improving parameter efficiency within MoEs. Extensive experiments across multiple downstream tasks demonstrate that ModularMoE outperforms other state-of-the-art baselines at the same sparsity level, achieving an average performance improvement of 4.10% to 28.75% while delivering up to 2.71× inference speedup.

Micro-enterprises and individual developers emerge analysis demands for long sequence with powerful Large Language Models (LLMs). They try to deploy the LLMs at local, but only possess various commodity devices and the unreliable interconnection between devices. Existing parallel techniques do not lead to the same effectiveness in limited environment. The heterogeneity of devices, coupled with their limited capacity and expensive communication, brings challenges to private deployment for maximized utilization of available devices while masking latency. Hence, we introduce HPipe, a pipeline inference framework that successfully mitigates LLMs from high-performance clusters to heterogeneous commodity devices. By ensuring a balanced distribution of workloads, HPipe facilitates the parallel execution of LLMs through pipelining the sequences on the token dimension. The evaluation conducted on LLaMA-7B and GPT3-2B demonstrates that HPipe holds the potential for context analysis on LLM with heterogeneity devices, achieving an impressive speedup in latency and throughput up to 2.28 times.