Sparse Mixture-of-Experts Transformers with Dynamic Routing for Efficient Large Language Model Inference
Abstract
We propose DynaMoE, a sparse Mixture-of-Experts (MoE) architecture with learned dynamic routing that achieves 2.8× inference speedup over dense Transformers of equivalent quality. Unlike conventional top-k gating, DynaMoE uses a lightweight auxiliary network to predict the optimal number of experts per token based on input complexity, allocating 2-8 experts dynamically. Evaluated on a 47B-parameter model trained on 1.2T tokens, DynaMoE matches GPT-4-level performance on MMLU (87.2%), HumanEval (82.3%), and GSM8K (94.1%) while reducing FLOPs per token by 64%. We provide theoretical analysis showing that dynamic routing preserves model expressiveness while enabling conditional computation, and release training code and model weights.
Keywords: mixture of experts, large language models, dynamic routing, efficient inference, sparse transformers
1. Introduction
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their deployment is constrained by enormous computational costs during inference. A 175B-parameter dense Transformer requires approximately 350 GFLOPs per generated token, creating significant challenges for real-time applications and edge deployment.
Mixture-of-Experts (MoE) architectures offer a compelling solution by activating only a subset of parameters per input token, enabling models to maintain large total parameter counts while reducing per-token computation. However, existing MoE implementations use fixed top-k routing (typically k=2), applying the same computational budget regardless of input complexity.
2. Method: DynaMoE Architecture
DynaMoE extends the standard MoE Transformer by replacing the fixed top-k router with a learned dynamic routing module. For each token, a lightweight auxiliary network (2-layer MLP with 256 hidden units) predicts both the number of experts to activate (k ∈ {2, 4, 6, 8}) and the routing probabilities across all available experts. The auxiliary network is jointly trained with the main model using a combined loss.
3. Experiments and Results
We trained DynaMoE-47B (47B total parameters, ~12B active per token on average) on a curated dataset of 1.2T tokens spanning web text, code, scientific literature, and instruction data. Training was conducted on 512 NVIDIA H100 GPUs for 21 days using ZeRO-3 parallelism with expert parallelism across 8 GPUs per node.
Table 1. Benchmark comparison of DynaMoE-47B against dense and sparse baseline models
| Model | Active Params | MMLU | HumanEval | GSM8K | FLOPs/token |
|---|---|---|---|---|---|
| Dense-13B | 13B | 72.4 | 61.2 | 68.5 | 1.0× |
| Dense-47B | 47B | 86.8 | 80.1 | 93.2 | 3.6× |
| MoE-47B (top-2) | 12B | 84.5 | 76.8 | 89.7 | 1.0× |
| DynaMoE-47B | ~12B avg | 87.2 | 82.3 | 94.1 | 1.28× |
4. Analysis
The dynamic routing mechanism effectively learns to allocate computation based on token difficulty. Analysis of the routing decisions reveals that mathematical reasoning and factual question-answering tokens activate 6-8 experts on average, while simple text continuation and formatting tokens typically use only 2-3 experts. This adaptive behavior explains why DynaMoE achieves accuracy comparable to the full dense model while maintaining inference costs close to the fixed top-2 MoE baseline.
5. Conclusions
DynaMoE demonstrates that dynamic expert allocation based on input complexity can bridge the quality gap between sparse MoE and dense Transformers while maintaining the computational efficiency of sparse models. The approach is general and can be applied to any MoE architecture, providing a practical pathway toward deploying high-quality LLMs at reduced inference costs.
References
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- Shazeer, N.; Mirhoseini, A.; Maziarz, K. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR 2017.
- Clark, A.; De Las Casas, D.; Guy, A. Unified Scaling Laws for Routed Language Models. ICML 2022.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).