GPU Graphics Processor

GPUs, with their highly parallel architecture, have become the primary hardware for deep learning training and inference. The NVIDIA CUDA ecosystem is the most mature, with toolchains like cuDNN, TensorRT, and Triton Inference Server covering the full workflow from research to deployment. AMD ROCm is rapidly catching up in the open-source community, with products like the MI300X demonstrating strong inference cost-performance and gradually gaining more native framework support. Intel leverages OpenVINO and Arc series for AI inference markets, focusing on edge and client scenarios. Consumer GPUs suit individual developer experimentation and small-batch inference, while data center-grade GPUs (such as H100, MI300X, Intel Gaudi 3) support large-scale training clusters. Apple's M-series unified memory architecture brings unique advantages for on-device inference, leading the experience for local large model execution. Domestically, Moore Threads, Biren Technology, MetaX, Iluvatar, Kunlunxin, T-Head, Jingjia Micro, and Vastai Semiconductor have all launched products, with ecosystems gradually maturing, showing broad application prospects in Xinchuang and intelligent computing center scenarios.

This category includes the following AI accelerator chips/compute cards:

• NVIDIA GPU / CUDA
• AMD ROCm / GPU
• Intel GPU
• Apple Silicon GPU
• Qualcomm Adreno GPU
• Moore Threads MTT S5000
• Biren Technology BR100/BR20X
• MetaX Xiyun C500/C600
• Iluvatar BI-150/BI-V150
• Baidu Kunlunxin P800
• Alibaba T-Head Zhenwu PPU
• Jingjia Micro JM9 Series
• Vastai Semiconductor SV Series