When you smoothly run a 3A game or watch an 8K video on your computer, the GPU (Graphics Processing Unit) working silently behind the scenes has long transcended its singular identity as a “graphics card.” It has become a core computing engine driving artificial intelligence, scientific computing, and financial analysis. Known as “GPU computing,” this technology is revolutionizing how we process data with its unique parallel architecture.
The Evolution of GPU’s Identity
The original purpose of GPUs was to handle pixel calculations in graphics rendering efficiently. Each frame of an image has millions of pixels to process. The color and brightness of each pixel follow the same computation logic. This need to “repeat processing large volumes of similar data” shaped GPU hardware. Its architecture is very different from that of CPUs. CPUs usually have 4 to 32 complex cores. They excel at tasks needing branch prediction and logical judgment. In contrast, GPUs have thousands of simplified computing cores. For example, NVIDIA’s latest GPUs have over 18,000 CUDA cores. These cores use SIMD (Single Instruction, Multiple Data) mode. This lets them execute tens of thousands of identical instructions at once.
The launch of NVIDIA’s CUDA platform in 2007 marked GPU’s official entry into the era of general-purpose computing. Through this comprehensive ecosystem—including compilers, libraries, and development tools—developers could directly leverage the parallel cores of GPUs to handle non-graphical tasks for the first time. Today, CUDA has evolved to version 12.9, supporting cutting-edge operating systems like Ubuntu 24.04. Its 25.05 version container image even pre-installs AI frameworks such as PyTorch and TensorRT, boosting deep learning development efficiency by 3 to 5 times. AMD has also introduced the ROCm ecosystem as a competitor, but CUDA currently holds approximately 80% of the professional GPU computing market share.
How GPUs Achieve Computing Leaps
The key to understanding GPU computing lies in distinguishing between “data parallelism” and “task parallelism.” Take the multiplication of two 1024×1024 matrices as an example: a CPU uses a relatively small number of powerful cores to perform calculations via multi-threading and vectorized instructions, but its parallel scale is orders of magnitude smaller than that of a GPU. A GPU, however, splits the matrix into thousands of 16×16 blocks and distributes them to different computing cores for simultaneous processing—much like thousands of people solving the same type of math problem at once.
This architectural difference leads to a huge computing gap between CPUs and GPUs. A typical CPU has single-precision floating-point capacity of 100–300 GFLOPS. NVIDIA’s GB200 GPU, by contrast, can reach 34 TFLOPS. That’s equal to the combined computing power of 100 CPUs. More importantly, GPU computing power grows in two ways. It benefits from both architectural innovation and process technology advancements. Its growth follows a “super Moore’s Law” trajectory. This trajectory far outpaces that of traditional CPUs. Over the past decade, CPU performance has risen by 15% annually on average. Meanwhile, GPU computing power has grown by over 50% each year.
Yet GPUs are not a one-size-fits-all solution. When handling tasks requiring frequent branch judgments (such as operating system scheduling), their simplified cores lack branch prediction units, leading to lower efficiency than CPUs. Consequently, modern computing systems generally adopt a collaborative “CPU-led, GPU-accelerated” model: CPUs manage task allocation and complex logical processing, while GPUs focus on large-scale data-parallel computing. The two transmit data at high speed via PCIe 5.0 interfaces, with latency typically in the microsecond range. In high-performance computing clusters, GPUs communicate directly with each other via dedicated interconnect technologies like NVLink to achieve even lower latency.
The 2025 Application Revolution: Four Frontier Fields of GPU Computing
In the field of artificial intelligence, GPUs have become the “infrastructure” for training large models. When OpenAI trained GPT-5, it used a cluster of 1,024 DGX systems, each equipped with 8 GPUs. The daily computing power consumed is equivalent to 7 billion people worldwide using calculators continuously for 300 years. The emerging GPU computing power rental market in 2025 has further enabled small and medium-sized enterprises to access top-tier computing power on demand. Tools like WhaleFlux—designed specifically for AI enterprises—optimize the utilization efficiency of multi-GPU clusters, offering purchase and rental services for mainstream GPUs such as NVIDIA H100, H200, A100, and RTX 4090 (with a minimum rental period of one month). These services help enterprises reduce cloud computing costs in areas like autonomous driving training and drug development, while also improving the deployment speed and stability of large language models.
The financial industry is leveraging GPUs to reshape risk control systems. High-frequency trading systems use GPU-accelerated Monte Carlo simulations to complete risk assessments in 1 millisecond— a task that previously took 1 second. Quantitative funds utilize GPUs to process 10TB of daily market data, uncovering subtle price fluctuation patterns. Tests by a leading securities firm showed that GPU-accelerated trading algorithms increased returns by 12% compared to traditional CPU-based solutions.
The integration of quantum computing and GPUs has opened up a new track. NVIDIA’s CUDA-QX toolkit can accelerate quantum error correction by 35 times, and its DGX Quantum system connects GPUs and quantum processors with sub-microsecond latency, addressing the classical data bottleneck in quantum computing. This “quantum-classical hybrid computing” model allows a 50-qubit system to achieve computing performance equivalent to that of a 100-qubit system.
Domestic GPU development has also made breakthroughs. In August 2025, Shanghai Lisan released its first self-developed architecture GPU chip, the 7G100. It reportedly supports NRSS dynamic rendering technology (comparable to DLSS) and performs close to NVIDIA A100 in specific test scenarios. Although the company is still in the red, enterprises like Dongxin Semiconductor have invested an additional 500 million yuan to accelerate mass production. The chip is expected to officially enter the consumer market in September 2025, breaking the monopoly of foreign manufacturers.
Future Challenges and Development Directions
The biggest challenge facing GPU computing is energy efficiency. Current top-tier GPUs consume up to 1,000 watts of power. That’s equivalent to the power used by a small air conditioner. Annual electricity costs for data center GPU clusters often exceed 100 million yuan. In 2025, manufacturers launched various energy-saving solutions. NVIDIA added dynamic voltage regulation to CUDA 12.9. This technology cuts GPU power consumption by 20%. AMD adopted 3D stacked memory for its GPUs. This helps lower energy use during data transmission. Meanwhile, GPU resource management tools like WhaleFlux play a role. They help enterprises optimize cluster utilization effectively. This reduces both computing costs and energy consumption for businesses. These tools are crucial to ease energy efficiency pressures.
Fragmentation in the software ecosystem also hinders development. While CUDA has a mature ecosystem, over-reliance on a single vendor poses risks; the open-source ROCm ecosystem, on the other hand, lacks unified standards, requiring separate optimization for GPUs from different manufacturers. To address this, the Khronos Group is developing the OpenCL 4.0 standard, scheduled for release in 2026. Its goal is to better unify programming models and reduce the cost for developers to port code across different hardware.
Looking ahead to 2030, GPUs will move toward “heterogeneous integration.” Companies like NVIDIA and Intel have begun developing integrated chips that combine GPUs, CPUs, and AI accelerators; the Chinese Academy of Sciences is exploring “photonic quantum GPUs,” which use photons to transmit data and have a theoretical computing power 1,000 times that of current GPUs. These innovations may redefine “GPUs” as computing genes embedded in various devices, rather than standalone hardware.
GPU computing has evolved from gaming graphics cards to the core engine of the digital economy. Its development shows the tech evolution law of “specialized hardware turning generalized.” Next time we marvel at lifelike AI-generated images, take a moment to think. Or when we’re impressed by accurate weather forecasts, remember this: behind these tech wonders are thousands of GPU cores. These cores compute the future at trillions of operations per second. Meanwhile, supporting systems keep improving. This includes resource management tools and computing power service platforms. They continue to inject new momentum into this computing revolution.
