GPU

What Is a GPU?

A graphics processing unit (GPU) is a specialized processor originally designed to accelerate the rendering of images, textures, and 3D geometry. Unlike a CPU, which excels at sequential tasks with a handful of powerful cores, a GPU contains thousands of smaller cores optimized for massively parallel computation. This architecture makes GPUs indispensable not only for real-time rendering and gaming, but also for the matrix-multiplication workloads at the heart of modern deep learning, large language model training, and AI inference. NVIDIA, AMD, and Intel are the dominant GPU designers, while foundries like TSMC fabricate the most advanced chips on cutting-edge process nodes.

GPUs and the AI Revolution

The explosion of generative AI has transformed GPUs from gaming peripherals into the most strategically important semiconductor components in the world. NVIDIA's data-center GPU revenue now dwarfs its gaming segment, driven by insatiable demand for chips like the H100 and the newer Blackwell B200 series. As of 2026, the global data-center GPU market is projected to exceed $138 billion, with hyperscalers — Amazon, Microsoft, Google, and Meta — collectively spending over $450 billion on AI infrastructure. The CUDA software ecosystem, which provides low-level access to NVIDIA GPU parallelism, has created a powerful moat that competing architectures from AMD (ROCm) and Google (TPUs) continue to challenge.

From Training to Inference: The Shifting Workload

A critical transition is reshaping GPU demand. While training frontier models still requires massive GPU clusters, inference now accounts for roughly 67% of total AI compute — up from about one-third in 2023. The rise of agentic AI and always-on AI assistants means GPUs must serve billions of real-time queries rather than periodic training runs. This shift is spurring new architectures optimized for inference throughput, including NVIDIA's acquisition of Groq technology for a dedicated Language Processing Unit (LPU) announced at GTC 2026. Meanwhile, the inference economy is driving a 1,000× cost collapse in per-token pricing, making AI applications economically viable at massive scale. Custom AI accelerators (ASICs) from cloud providers are projected to grow shipments 44% in 2026, compared to 16% for traditional GPUs, signaling a diversifying compute landscape.

GPUs in Gaming and Spatial Computing

GPUs remain the engine of interactive entertainment and immersive experiences. NVIDIA's RTX 5090, built on the Blackwell architecture, delivers 70 petaflops of FP4 performance with hardware-accelerated ray tracing and DLSS 4 AI upscaling, while AMD's RDNA 5-based RX 9070 XT competes aggressively on rasterization performance at lower price points. Technologies like WebGPU are bringing GPU-accelerated graphics and compute to the browser, expanding the reach of interactive 3D content. For the metaverse and spatial computing, GPUs must simultaneously handle physics simulation, neural rendering, AI-driven NPC behavior, and low-latency stereoscopic output — workloads that continue to push the limits of parallel processing.

Supply Chains, Power, and the Future

The GPU industry faces structural bottlenecks that constrain the pace of AI expansion. High Bandwidth Memory (HBM) availability and advanced chip packaging are the true limiting factors — TSMC's 3nm fabs are sold out through 2028. Energy consumption is another critical constraint: AI data center power demand in the United States could reach 123 gigawatts by 2035, up from 4 gigawatts in 2024. These pressures are driving innovation in chiplet architectures, optical interconnects, and liquid cooling, while also prompting a geopolitical race for sovereign AI infrastructure. As the agentic economy matures, GPUs will remain the foundational compute layer — even as the ecosystem diversifies with specialized inference chips, neuromorphic processors, and GPU cloud platforms that democratize access to high-performance computing.