Among the parameter-efficient fine-tuning (PEFT) methods, LoRA has shown significant advantages in reducing the cost of fine-tuning large language models (LLMs), but its limited representational capacity still constrains performance. Recent studies have attempted to incorporate a Mixture-of-Experts (MoE) structure into LoRA to further enhance representational capacity (MoE-LoRA), but has introduced additional computational overhead. This trade-off highlights a key challenge in the advancement of PEFT methods: how to strengthen representational capacity without sacrificing efficiency. To address this challenge, we revisit the LoRA architecture and find that its intermediate representation naturally forms well-separated clusters after splitting. Motivated by this observation, we propose MKLoRA, which partitions the intermediate representation into multiple subspaces for efficient knowledge collaboration, aggregates knowledge through a shared up-projection matrix, and further optimizes learning with an asymmetric learning-rate strategy. MKLoRA improves representational capacity without increasing the number of trainable parameters, consistently surpassing LoRA variants in single-task and multi-task instruction tuning, while reducing computational overhead to just 57% of MoE-LoRA.