This article explains why GPU virtualization is needed, compares resource‑sharing and isolation approaches, and details user‑level API interception, remote API forwarding, half‑virtualization with virtio, kernel‑level driver interception, and hardware‑level solutions such as vGPU, MIG, and AMD MxGPU.
This article provides a comprehensive overview of desktop cloud solutions, describing their architecture, deployment models, key virtualization technologies such as VDI and IDV, hardware components, industry participants, application scenarios across sectors, and emerging trends like vGPU and market competition.
The article explains how massive parallel pixel calculations demand GPUs, whose high cost and inflexibility are solved by Tencent Cloud’s elastic, virtualized GPU services—including vGPU, qGPU, TACO abstraction, and spot instances—delivering up to 16 EFLOPS for AI, scientific, graphics, and video workloads.
This article explains NVIDIA GRID vGPU virtualization, detailing how GPUs are partitioned by memory size, the supported hypervisors, the operation of virtual GPU resources, differences between full‑allocation vGPU and GPU pass‑through, licensing requirements, and performance considerations for cloud and data‑center environments.
