Scaling L0-MoE to larger language models

Determine whether scaling the L0-MoE architecture to larger language models, including models with approximately 70 billion parameters, can achieve greater inference speedups while maintaining performance comparable to the corresponding dense models.

Background

The paper evaluates L0-MoE on Qwen2-1.5B, Qwen2-7B, Mistral-7B, and Llama-3-8B models, reporting inference speedups of approximately 2–2.5× without substantial benchmark degradation. The authors state that computational resource constraints prevented experiments with larger models, such as 70B-parameter models.

The unresolved issue is whether the observed acceleration and performance preservation will extend to substantially larger dense LLMs. The authors hypothesize that larger models may achieve even greater speedups, but leave this hypothesis unverified for future work.

References

However, due to computational resource constraints, we have not yet experimented with larger models (e.g., 70B parameters). We hypothesize that larger LLMs could potentially achieve even greater speedups. We leave the verification of this hypothesis for larger-scale models as future work.

— Accelerating Dense LLMs via L0-regularized Mixture-of-Experts  (2609.21672 - Zhang et al., 18 Sep 2026) in Section 4, Discussion; Section 5, Conclusion and Future Work