The article examines how AMD, NVIDIA, and Intel are expanding their high‑performance computing portfolios through acquisitions and product development, focusing on FPGA, AI accelerators, and cloud‑centric strategies, and evaluates the technical and market implications of these moves.
The article explains GPU evolution, its integration with CPUs, interconnect technologies like PCIe and NVLink, market shares of NVIDIA, AMD and Intel, AI accelerator types (GPU, FPGA, ASIC), and the roles of training and inference in cloud AI, while also promoting a paid 182‑page PPT resource.
At NVIDIA’s GTC China 2020, chief scientist Bill Dally highlighted the “Huang’s Law” predicting GPU-driven AI performance to double biennially, introduced projects like MAGNet, optical interconnects, and the Legate programming model, and discussed the broader implications for AI ecosystem development and industry adoption.
This guide walks through installing NVIDIA drivers, CUDA toolkit, nvidia-container-runtime, configuring Containerd, deploying the NVIDIA device plugin, and testing GPU access inside Kubernetes pods, providing a complete solution for GPU workloads on containerd‑based clusters.
The article introduces NVIDIA’s third‑generation NVLink technology, detailing its high‑bandwidth GPU‑GPU and GPU‑CPU interconnect, key architectural breakthroughs such as the Ampere‑based A100 GPU, multi‑instance GPU, and NVSwitch, and discusses its impact on AI, HPC, and graphics workloads.
Nvidia’s $40 billion purchase of Arm from SoftBank merges its AI chip expertise with Arm’s vast processor ecosystem, promising new AI research centers, open licensing, and a strategic edge against rivals while reshaping the future of computing hardware.
Bill Dally, Nvidia’s chief scientist, designed a low‑cost, open‑source mechanical ventilator using off‑the‑shelf components that can be assembled in minutes for about $400, aiming to alleviate COVID‑19 ventilator shortages, while collaborating with experts across AI, robotics, and medical fields.
The article explains how CPUs and GPUs differ in architecture and workload handling, details key GPU specifications such as CUDA cores, memory bandwidth and floating‑point precision, reviews NVIDIA's product families and architectural evolution, and highlights the role of GPUs in deep learning training and inference while also mentioning a related technical ebook promotion.
This article explains the fundamental differences between CPUs and GPUs, outlines key GPU specifications such as CUDA cores, memory capacity, bandwidth, and floating‑point precision, and reviews NVIDIA's major GPU series and architectural evolution for high‑performance and AI workloads.
This article explains NVIDIA's NVLink interconnect technology, covering its history, protocol layers, bandwidth advantages over PCIe, topologies such as the HGX-1/DGX-1 mesh, the NVSwitch extension, and performance gains for deep‑learning and high‑performance computing workloads.
The article surveys how NVIDIA's GPU ecosystem and the Intersect360 study reveal that among the 50 most common high‑performance computing applications, 34 already support GPU acceleration, highlighting the growing importance of GPU computing across scientific, engineering, and business fields.
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.
This article compares NVIDIA's Tesla and GeForce GPU families, detailing their target markets, design differences, core architectures, double‑precision performance, ECC support, memory bandwidth, interface options, software and OS compatibility, power efficiency, and management features to help readers choose the right GPU for HPC or gaming workloads.
