The field of hardware acceleration for Large Language Models (LLMs) is rapidly advancing, with a focus on optimizing computational efficiency and energy consumption. Researchers are exploring specialized hardware architectures, such as RISC-V accelerators, to improve performance and reduce power consumption. Additionally, there is a growing interest in automatically generating high-performance tensor operators with hardware primitives, which can significantly improve the performance of LLMs on diverse hardware platforms. Another area of research is challenging the conventional assumption that GPUs always provide the best performance for LLM inference, with some studies demonstrating that CPUs can outperform GPUs under certain conditions. Noteworthy papers include: Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities, which evaluates the performance of the Tenstorrent Grayskull e75 RISC-V accelerator, and QiMeng-TensorOp, which introduces a tensor-operator auto-generation framework that achieves up to 1291x performance improvement compared to vanilla LLMs.