ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
arXiv:2605.27081v1 Announce Type: new Abstract: Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining high model capacity. However, in memory-constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched from slow external storage (e.g., UFS), leading to frequent evictions and substantial I/O overhead. We propose ReMoE, a router fine-tuning framework designed to boost token-wise expert reuse. ReMoE biases the router toward recently selected expert
The rapid scaling of LLMs has exposed memory and computational bottlenecks, making efficient inference a critical challenge, especially for MoE architectures.
Improving MoE LLM inference efficiency directly impacts the cost and accessibility of large AI models, potentially democratizing access to advanced AI capabilities.
This innovation changes how Mixture-of-Experts models utilize memory during inference, leading to more efficient expert reuse and reduced I/O overhead.
- · AI compute infrastructure providers
- · Cloud providers
- · AI model developers
- · Organizations deploying large language models
- · Less efficient LLM architectures
- · Traditional, memory-intensive inference methods
More widespread deployment of large MoE LLMs due to reduced inference costs and improved performance.
Increased competition among AI model developers as previously infeasible model sizes become more practical to deploy.
Acceleration of AI applications requiring real-time, high-throughput language processing in memory-constrained environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG