With the widespread deployment of large language models (LLMs) across various generative tasks, their high computational demands impose stringent performance requirements on the underlying hardware. RISC-V, an emerging open-source instruction-set architecture, shows great potential owing to its excellent customizability and extensibility. Nevertheless, when deploying mainstream LLMs, the RISC-V ecosystem still faces challenges such as an incomplete software stack and limited compute capability. This study proposes an inference acceleration method for LLMs on RISC-V heterogeneous platforms. By establishing a heterogeneous runtime environment that integrates the Cambricon MLU370 accelerator, the device driver is ported, essential libraries are compiled, and the PyTorch framework is adapted. Building on this foundation, a lightweight multi-threading optimization scheme is further designed to improve the efficiency of core operators—especially the attention mechanism—on multi-core architectures. Experimental results on the SG2042+ MLU370-S4 platform show that, without relying on any additional optimizations, the proposed method achieves up to 52.3 times end-to-end inference speedup for several mainstream LLMs, thus demonstrating both the feasibility and broad applicability of the approach on RISC-V heterogeneous systems.