Code LLMs exhibit remarkable capabilities in automated code generation, but the generated code often suffers from low computational efficiency. Existing solutions have two limitations: (1) direct fine-tuning lacks transferability across model scales; (2) The efficiency optimization method for iterative generation-evaluation loops incurs high overhead. We propose CoEffi, which adopts inference-side intervention that directly operates on the decoding process without modifying base model parameters, enabling real-time efficiency improvement across different model scales while avoiding iterative reevaluation. CoEffi uses a small efficiency expert model for proposal and the base model to evaluate its acceptability, additionally introducing a region-aware dynamic acceptance threshold mechanism that maintains a sliding window to record historical acceptance rates and adjusts the dynamic threshold accordingly. We extensively evaluate CoEffi on four mainstream code LLM families and three efficiency benchmarks. Compared with vanilla instruction-tuned models, CoEffi improves average Beyond@1 by 9.12%–38.62% across the evaluated model families. It also improves Pass@1 on Mercury and ENAMEL, while EffiBench exhibits model-dependent correctness gains and regressions. A family specific trained expert can be reused across compatible base model scales within the same model family.