AI Training Card Market Size, Trends & Forecasts | Valuates Reports

AI Training Card Market Size

The global market for AI Training Card was valued at US$ 23865 million in the year 2024 and is projected to reach a revised size of US$ 115079 million by 2031, growing at a CAGR of 25.2% during the forecast period.

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AI training cards are high-performance computing hardware designed specifically for training artificial intelligence models. They are usually based on graphics processing units (GPUs), tensor processing units (TPUs), or other specialized accelerators, capable of processing large amounts of data and performing complex computing tasks. These cards play a vital role in the AI model training process, as AI training requires a lot of computing power to process and analyze data, adjust model parameters, and improve model accuracy and performance.

As the core hardware that supports efficient training and reasoning of artificial intelligence models, AI training cards are in a stage of rapid development. As large models (such as GPT, LLaMA, and Claude) place higher demands on computing power and video memory capacity, the global market demand for high-computing GPUs (such as NVIDIA H100), dedicated AI accelerators (such as TPU and Ascend), and heterogeneous chips (such as FPGA+NPU combinations) continues to rise. NVIDIA has firmly established its leading position in the high-end AI training market with its CUDA ecosystem and powerful software and hardware integration advantages; while manufacturers such as AMD, Google, Graphcore, and Cerebras are also constantly catching up in technological breakthroughs and architectural innovations. At the same time, Chinese local manufacturers such as Huawei (Ascend) and Cambricon are accelerating their layout in promoting domestic substitution, independent control, and AI computing infrastructure construction, especially in the large-scale application of government affairs, finance, security, and other scenarios. Overall, the AI training card market is moving from "hardware stack performance" to the competition stage of "software and hardware collaboration + heterogeneous integration". The future trend will focus on ultra-high bandwidth memory (HBM3/4), low power consumption and high computing power ratio, intelligent resource scheduling, and support for multi-modal large models. As AI technology continues to evolve, AI training cards will also play an increasingly critical role in the localization process, global AI infrastructure construction, and the commercialization of AI services.

By Type
• Cloud
• Terminal

By Application
• 2000 TOPS

Key Companies
Intel, NVIDIA, AMD, Cerebras, Graphcore, Qualcomm, IBM, Moore Threads, Cambricon, Huawei, Goke Microelectronics, MetaX, Hygon, Iluvatar, Birentech, Kunlunxin

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This release was published on openPR.