GPU Technology Overview: Architecture, Market Landscape, and Key Application Directions

GPU advantages lie in its many cores, small per‑core cache, and simple logic units; the GPU market has entered an oligopoly era dominated by Intel, NVIDIA and AMD.

Intel leverages its CPU dominance to secure a strong position in integrated GPUs, while NVIDIA and AMD lead the independent GPU segment; the analysis concludes that extended R&D through acquisitions and the superior performance of independent GPUs will drive future growth.

Application direction one focuses on the pursuit of an optimal balance between entertainment and performance, noting the rapid growth of the global gaming market, the rise of gaming laptops with ray‑tracing support, and the trend toward thinner yet powerful notebooks.

Application direction two examines artificial intelligence and deep learning, explaining that GPUs excel in the training phase due to parallel computation, and remain a primary chip for inference while FPGA and ASIC technologies mature.

Application direction three discusses autonomous driving, highlighting that GPUs’ parallel processing capabilities are well‑suited for handling massive sensor and image data, making them the mainstream solution in this field.

GPU (Graphics Processing Unit) is defined as a specialized micro‑processor for image and graphics computation in PCs, workstations, game consoles and mobile devices; originally, graphics tasks were performed by CPUs until dedicated graphics accelerators and later GPUs were introduced.

Compared with CPUs, GPUs have far more cores (hundreds versus a few), smaller caches, and simpler logic units, making them better suited for data‑parallel workloads.

GPUs are classified by their relationship to the CPU (integrated vs. independent) and by application domain (PC, server, mobile). Integrated GPUs share resources with the CPU, offering better compatibility and lower cost, while independent GPUs have dedicated memory, higher performance, larger power consumption and higher cost.

PC GPUs can be either integrated or independent depending on usage scenarios; server GPUs are primarily independent to support professional visualization, compute acceleration and deep learning; mobile GPUs are usually integrated due to space constraints.

The PC GPU market is dominated by Intel’s integrated solutions, AMD’s combined integrated and independent offerings, and NVIDIA’s independent GPUs, with AMD’s recent 7 nm Radeon series challenging NVIDIA on performance and price.

In the mobile GPU arena, major players include Imagination, ARM, Qualcomm, Vivante and NVIDIA; Qualcomm leads the Android market, while ARM‑based GPUs are used by Huawei and Samsung, which are also pursuing self‑developed GPU IP.

Artificial intelligence is a major growth driver, with global AI market projected to exceed $6 trillion by 2025 and a CAGR of 30 % from 2017‑2025; AI development relies on three pillars: data, compute (GPU‑driven), and algorithms.

Deep learning consists of training (GPU‑heavy parallel computation) and inference (where CPUs, GPUs, FPGAs or ASICs may be used depending on precision and latency requirements).

Autonomous driving generates massive data streams (e.g., 12 cameras producing >12 GB/s), requiring high‑throughput parallel processing that GPUs provide; the prevailing architecture combines GPU and CPU, as FPGA solutions remain complex and costly for most startups.