After NVIDIA proposed the concept of GPU in 1999, this chip has undergone a transformation from being an auxiliary graphics device to driving the global technological revolution. Let's share together the development history of cpus by taking the breakthroughs in GPU technology, the expansion of application scenarios, and the changes in the industrial landscape as the clues.
I. The Birth and Early Competition of Graphics Processors (1980-2000)
The prototype of GPU can be traced back to the graphics acceleration cards in the 1980s. In 1981, IBM launched its first personal computer, the 5150. The MDA (Monochrome Display Adapter) and CGA (Color Graphics Adapter) it was equipped with could only complete basic graphics output, and the computing was completely dependent on the CPU. In 1991, S3 Graphics launched its first 2D acceleration chip, 86C911, marking the beginning of the era of graphics hardware acceleration. In 1994, Glint 300SX released by 3DLabs first supported 3D rendering, but it had a single function and lacked a unified standard.
The real turning point occurred in 1999: NVIDIA launched the GeForce 256, integrating hardware-level T&L (Geometric Transformation and Illumination Calculation) units for the first time, transferring graphics processing tasks from the CPU to dedicated chips, and officially registering the "GPU" trademark. This chip, which adopts a 220nm process and integrates 17 million transistors, enabled games such as Quake III to achieve dynamic light source physical simulation for the first time, laying the foundation for the architecture of modern Gpus. Meanwhile, 3dfx missed out on Microsoft's Xbox order due to its adherence to a closed ecosystem and was eventually acquired by NVIDIA. The market competition pattern began to show a trend of concentration.
Ii. The Programmable Era and the Rise of General Computing (2000-2010)
In 2001, Microsoft DirectX 8 introduced programmable vertex shaders, and NVIDIA first implemented hardware programmability in GeForce 3, initiating the transformation of Gpus from fixed pipelines to flexible computing. In 2006, NVIDIA released the GeForce 8800 GTX based on the G80 architecture, adopting a unified rendering architecture that merged vertices and pixel shaders, increasing resource utilization by 40%. The CUDA platform launched in the same year was even more revolutionary, allowing developers to invoke GPU computing power using the C language for general computing. Although initially questioned for deviating from the main business of gaming, this decision laid the groundwork for the subsequent explosion of AI.
Iii. The Deep Learning Revolution and the AI Computing Power Hegemony (2012-2020)
The breakthrough achievement of AlexNet in 2012 completely rewrote the fate of Gpus. With the help of two GTX 580 Gpus, this model won the championship in the ImageNet competition with an error rate of 15.3%, and its efficiency was 100 times higher than that of the traditional CPU solution. Nvidia promptly adjusted its strategy: In 2016, the Pascal architecture was specifically optimized for deep learning; in 2017, the Volta architecture introduced Tensor Core to accelerate matrix operations; and in 2018, the Turing architecture integrated RT Core to support real-time tracing. In 2020, the A100 GPU based on the Ampere architecture, with its sparse matrix computing capability, increased the training speed of Transformer models by six times.
During this period, the GPU market presented a "dual-track differentiation" : The consumer market (such as the RTX 30 series) continued to enhance gaming performance, while data center Gpus (such as H100) became the core of the AI infrastructure. In 2024, NVIDIA's GB200 chip based on the Blackwell architecture will enhance the inference performance of large language models by 30 times and reduce energy consumption by 25%, consolidating its dominant position in the field of generative AI.
Iv. Reshaping the Industrial Pattern and Future Challenges (2020- Present)
The current GPU industry is facing three major changes. The first change is the competition in technological routes. Specialized chips (such as Google's TPU and Huawei's Ascend) challenge the universality of Gpus in specific scenarios. AMD's CDNA architecture and Intel's Ponte Vecchio attempt to share the data center market.
The second transformation: The impact of geopolitical factors on the US export control has given rise to a wave of domestic substitution. Chinese enterprises such as Jingjia Microelectronics and Suiyuan Technology are accelerating their pursuit, but their performance still lags behind international flagship products by 510 years.
The third transformation is the ecological game. Qualcomm, Intel and others have established the "UXL Alliance" to promote cross-platform programming standards, attempting to break the monopoly of the CUDA ecosystem. Meanwhile, NVIDIA has restricted the transplantation of CUDA code through protocols to maintain its technological barriers.
Market data shows that the global GPU market size is expected to reach 35 billion US dollars in 2025, with AI training demand accounting for more than 60%. The evolution history of GPU is essentially a history of humanity breaking through the boundaries of computing.
