Large language models typically support different inference budgets through separate model sizes or fixed compute configurations.
In multi-turn interactions, however, compute demand can vary substantially across turns.
Using a high-compute model for every turn wastes computation, while routing among independent models may require rebuilding model-specific history states after switches.
We therefore ask whether a single shared model can support multiple compute budgets that can be flexibly assigned across turns.
We introduce Multi-Ratio ConceptMoE, which jointly supports token-to-concept compression ratios of 2, 4, and 8 (CR2/CR4/CR8) within a shared backbone.
Lightweight full-token modules reduce compression-independent cost, ratio-specific boundary routers support distinct compression levels, and cross-ratio training enables mixed-ratio processing within the same context.
Across PTBench and SFTBench, the three fixed modes outperform their compute-matched MoE baselines in all 6 comparisons while remaining competitive with independently trained fixed-ratio specialists.
On CodeChat and CoSQL, mixed-ratio inference improves the accuracy--compute trade-off, outperforming the Large MoE by 5.87 and 2.68 percentage points while reducing FLOPs by 41.1% and 12.3%, respectively.
Under the same turn-wise budget schedule, Multi-Ratio ConceptMoE further reduces FLOPs by 78.4% and 46.9% relative to routing among independent models.
These results demonstrate that a single shared model can provide multiple compute budgets for flexible compute allocation across turns.