Dermot explained that in large language model (LLM) reasoning, CPU and GPU complement each other to achieve the best performance and cost-effectiveness. More importantly, the CPU is not only the core control unit in the AI reasoning process, but also works with heterogeneous units such as GPU, NPU, FPGA and ASIC to manage and schedule system resources. As a core component in a heterogeneous computing architecture that efficiently processes AI workloads, Arm CPU can seamlessly integrate and collaborate with AI accelerators such as GPU and NPU, making it the only choice for various AI reasoning tasks. The unique advantages of CPU in the field of reasoning include:
1. Versatility and flexibility
2. Cost and energy efficiency advantages
For different industries and application scenarios, Arm adheres to the overall thinking at the system level to achieve seamless integration of hardware, software and ecological resources. In the field of infrastructure, with the popularity of generative AI and large language models, data centers need to balance efficient computing power, energy efficiency and rapid deployment. Arm has launched a complete set of targeted solutions for this purpose.