The chips used in AI servers

The chips used in AI servers mainly include the following types:

### CPU (Central Processing Unit)
- **Function**: As the core component of a computer, it is responsible for processing the computer's information and controlling its operation, executing computer system instructions and operating data.
- **Examples**: Intel Xeon series, AMD EPYC series, etc. The Intel Xeon Scalable processors have a high number of cores, multi-threading processing capabilities, and large-capacity high-speed caches. They can efficiently handle various complex computing tasks and data management work, providing powerful general-purpose computing capabilities for AI servers to meet the needs of multi-task processing and system management.

### GPU (Graphics Processing Unit)
- **Function**: Adopting a parallel computing architecture with a large number of cores, it is suitable for handling large-scale and highly parallel tasks. In AI training and inference, it can accelerate the parallel processing of a large amount of data such as matrix operations, thereby significantly improving computing efficiency. It is especially applicable to tasks like image recognition, speech recognition, and natural language processing in deep learning.
- **Examples**: NVIDIA's A100, H100 series, AMD's MI300, etc. NVIDIA's H100 GPU, based on a new architecture and manufacturing process, has a large number of CUDA cores and high-speed video memory. Its AI performance in FP8 precision is six times higher than that of the previous generation and can provide a bandwidth of 900GB/s, which can meet the requirements of large-scale AI model training and high-performance computing.

### TPU (Tensor Processing Unit)
- **Function**: A special-purpose processor developed by Google, specifically designed to accelerate the computing tasks of artificial intelligence. It is deeply optimized for AI applications such as machine learning and deep learning. When handling specific AI workloads, it can provide higher performance and lower power consumption compared to traditional CPUs and GPUs.
- **Examples**: Google's second-generation TPU chip. Its performance has been significantly improved compared to the first generation. It supports more operations and higher computing precision and has been widely used in large-scale data centers, providing powerful computing support for various AI services and research projects of Google.

### FPGA (Field Programmable Gate Array)
- **Function**: It has programmability and flexibility. Users can program and configure it according to specific application requirements to implement different logical functions and algorithms. In the AI field, FPGA can conduct customized hardware acceleration according to specific AI models and algorithms, and is suitable for some AI application scenarios with high requirements for flexibility and real-time performance.
- **Examples**: Xilinx's Virtex series, Intel's Stratix series, etc. Xilinx's Virtex UltraScale+ FPGA provides high-bandwidth, low-latency interconnection resources and abundant logical units, which can meet the high-performance requirements for data transmission and processing in AI systems. Moreover, it can implement various AI algorithms and neural network architectures through programming, providing customized hardware solutions for different AI applications.

### ASIC (Application Specific Integrated Circuit)
- **Function**: It is an integrated circuit designed for specific applications, with a high degree of customizability and optimization. It can conduct in-depth optimization for a certain type of specific AI task to achieve efficient computing and processing. Usually, it has advantages in terms of performance, power consumption, and cost, but its flexibility is relatively poor.
- **Examples**: Cambricon's Siyuan series chips, Bitmain's chips, etc.