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NVIDIA

235 articles · Page 3 of 3
DataFunTalk
DataFunTalk
Apr 1, 2023 · Artificial Intelligence

Nvidia Meets OpenAI: Highlights from the GTC Fireside Chat on GPT‑4, Deep Learning History, and the Future of AI

In a GTC fireside chat, Nvidia CEO Jensen Huang and OpenAI co‑founder Ilya Sutskever discuss GPT‑4's multimodal advances, the evolution of deep learning from early neural networks to large‑scale models, the pivotal role of GPUs and datasets like ImageNet, and their vision for more reliable, scalable artificial intelligence.

GPT-4MultimodalNVIDIA
0 likes · 10 min read
Nvidia Meets OpenAI: Highlights from the GTC Fireside Chat on GPT‑4, Deep Learning History, and the Future of AI
DataFunSummit
DataFunSummit
Feb 15, 2023 · Artificial Intelligence

ChatGPT Boom Fuels Surge in AI Chip Demand, Boosting Nvidia, Samsung, and SK Hynix

The explosive growth of ChatGPT and other AI chatbots is driving unprecedented demand for high‑performance AI chips and high‑bandwidth memory, positioning Nvidia as the primary beneficiary while also creating significant market opportunities for Samsung, SK Hynix, and other semiconductor manufacturers.

AI chipsAI hardwareChatGPT
0 likes · 11 min read
ChatGPT Boom Fuels Surge in AI Chip Demand, Boosting Nvidia, Samsung, and SK Hynix
Open Source Linux
Open Source Linux
Dec 1, 2022 · Fundamentals

How NVIDIA Boosted Software Safety by Switching from C to SPARK

NVIDIA’s security team adopted the formally verified SPARK language, replacing C in safety‑critical components, and after a successful proof‑of‑concept demonstrated improved security, verification efficiency, and unchanged performance, leading to widespread internal adoption across many products.

AdaCoreC to SPARK migrationNVIDIA
0 likes · 4 min read
How NVIDIA Boosted Software Safety by Switching from C to SPARK
DataFunTalk
DataFunTalk
Oct 31, 2022 · Artificial Intelligence

NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance

This article introduces NVIDIA Merlin's HugeCTR recommendation system framework, covering its three main modules—NV Tabular, HugeCTR, and Triton—detailing model‑parallel embedding handling, CUDA kernel fusion, mixed‑precision training, hierarchical parameter server inference, Sparse Operation Kit for TensorFlow, performance benchmarks, and practical deployment considerations.

EmbeddingGPU AccelerationHugeCTR
0 likes · 19 min read
NVIDIA Merlin HugeCTR: System Overview, Architecture, and Performance
Architects' Tech Alliance
Architects' Tech Alliance
Aug 18, 2022 · Cloud Computing

NVIDIA BlueField DPU Series: Architecture, Features, and Ecosystem Overview

The article provides a comprehensive overview of NVIDIA's BlueField DPU series—including BlueField‑2,‑3, and‑4—detailing their high‑performance architecture, network, security, and storage capabilities, as well as the DOCA development ecosystem that enables programmable acceleration for modern cloud data‑center workloads.

BlueFieldCloud ComputingDPU
0 likes · 12 min read
NVIDIA BlueField DPU Series: Architecture, Features, and Ecosystem Overview
Architects' Tech Alliance
Architects' Tech Alliance
May 23, 2022 · Industry Insights

GPU Wars in the Data Center: How Nvidia, AMD, and Intel Compete for AI and HPC Dominance

The article examines how GPUs have evolved from gaming accelerators to essential data‑center processors for AI, HPC, and scientific workloads, and compares the latest server‑grade offerings from Nvidia, AMD, and Intel—including performance specs, memory technologies, interconnects, and software ecosystems—highlighting the fierce competition shaping the future of compute.

AIAMDData Center
0 likes · 12 min read
GPU Wars in the Data Center: How Nvidia, AMD, and Intel Compete for AI and HPC Dominance
21CTO
21CTO
May 13, 2022 · Fundamentals

Why Nvidia’s Open‑Source GPU Driver Could Transform Linux and AI Development

Nvidia’s release of the open‑source R515 GPU driver for Linux, supporting data‑center and consumer GPUs under a dual GPL/MIT license, marks a pivotal shift that eases integration for AI/ML developers, gamers, and cloud users while fostering community‑driven improvements to driver quality and security.

AI/MLAmpereGPU Driver
0 likes · 7 min read
Why Nvidia’s Open‑Source GPU Driver Could Transform Linux and AI Development
Architects' Tech Alliance
Architects' Tech Alliance
May 4, 2022 · Industry Insights

What the Next‑Gen Nvidia and AMD GPUs Could Mean for the 2022‑2023 Market

Based on recent leaks from 3DCenter.org and Twitter insiders Kopite7kimi and 暴龙兽55, the article forecasts Nvidia's Lovelace RTX 4000 series and AMD's RDNA 3 Navi 33/32 GPUs to launch between September 2022 and early 2023, analyzes their expected specifications, pricing dynamics, and potential market impact, and notes Intel's upcoming Arc cards as a wildcard.

AMDGPULovelace
0 likes · 7 min read
What the Next‑Gen Nvidia and AMD GPUs Could Mean for the 2022‑2023 Market
IT Services Circle
IT Services Circle
Mar 24, 2022 · Artificial Intelligence

NVIDIA Unveils H100 GPU with Hopper Architecture: Massive Performance Gains for AI

At the recent GTC event, NVIDIA introduced the H100 GPU built on the Hopper architecture using TSMC 4nm process, featuring 800 billion transistors, 16,896 CUDA cores, up to 700 W power, 3 TB/s memory bandwidth, and a specialized Transformer engine that accelerates large‑model training up to six times faster, alongside the Grace CPU Superchip and new AI supercomputing systems.

AIGPUGrace CPU
0 likes · 11 min read
NVIDIA Unveils H100 GPU with Hopper Architecture: Massive Performance Gains for AI
Architects' Tech Alliance
Architects' Tech Alliance
Dec 14, 2021 · Industry Insights

Why NVIDIA’s BlueField DPU Is Redefining Data‑Center Architecture

The article provides an in‑depth analysis of NVIDIA’s BlueField DPU series—detailing the roadmap from BlueField‑2 to BlueField‑4, the technical capabilities of BlueField‑3 across networking, security, and storage, and the DOCA ecosystem that enables programmable, hardware‑accelerated data‑center services, positioning DPUs as a core pillar of modern cloud infrastructure.

BlueFieldDPUData Center
0 likes · 14 min read
Why NVIDIA’s BlueField DPU Is Redefining Data‑Center Architecture
Liangxu Linux
Liangxu Linux
Aug 17, 2021 · Cloud Native

How to Enable GPU Acceleration in Docker on Linux

This guide walks you through installing NVIDIA drivers, CUDA, and nvidia-docker2 on a Linux host, configuring Docker to access the GPU, and verifying the setup with commands and sample TensorFlow/PyTorch code, enabling deep‑learning workloads inside containers.

CUDADockerGPU
0 likes · 7 min read
How to Enable GPU Acceleration in Docker on Linux
DataFunTalk
DataFunTalk
Jun 13, 2021 · Artificial Intelligence

GPU Virtual Sharing for AI Inference Services on Kubernetes

The article presents a GPU virtual‑sharing solution for AI inference workloads that isolates memory and compute resources via CUDA API interception, integrates with Kubernetes using the open‑source aliyun‑gpushare scheduler, and demonstrates doubled GPU utilization and minimal performance loss across multiple tests.

CUDAGPU virtualizationNVIDIA
0 likes · 16 min read
GPU Virtual Sharing for AI Inference Services on Kubernetes
Architects' Tech Alliance
Architects' Tech Alliance
Mar 20, 2021 · Fundamentals

Evolution of NVIDIA GPU Architectures from Fermi to Ampere

This article outlines the progression of NVIDIA GPU architectures—from the early Fermi and Kepler designs through Maxwell, Pascal, Volta, Turing, and the latest Ampere—detailing compute capabilities, SM structures, FP64/FP32 ratios, Tensor Core introductions, and their impact on AI and high‑performance computing.

AICUDAGPU architecture
0 likes · 19 min read
Evolution of NVIDIA GPU Architectures from Fermi to Ampere
Architects' Tech Alliance
Architects' Tech Alliance
Mar 15, 2021 · Artificial Intelligence

Evolution of NVIDIA GPU Architectures from Fermi to Ampere

This article provides a comprehensive overview of NVIDIA's GPU architecture evolution—covering Fermi, Kepler, Maxwell, Pascal, Volta, Turing, and Ampere—detailing compute capabilities, SM structures, specialized units such as Tensor Cores, and their impact on AI and high‑performance computing workloads.

AICUDAGPU
0 likes · 19 min read
Evolution of NVIDIA GPU Architectures from Fermi to Ampere
JD Cloud Developers
JD Cloud Developers
Feb 8, 2021 · Artificial Intelligence

This Week’s Must‑Read Tech & AI Highlights: From Digital Currency to Cutting‑Edge Research

The developer community weekly roundup covers a digital RMB lottery on JD, AI data‑annotation market trends, Google Drive’s enterprise‑personal merge, Windows 10 cloud configuration, NVIDIA’s breakthrough in real‑time SDF rendering and A100 performance, plus new research on intent discovery and EEG‑based emotion recognition.

AICloud ComputingNVIDIA
0 likes · 8 min read
This Week’s Must‑Read Tech & AI Highlights: From Digital Currency to Cutting‑Edge Research
Architects' Tech Alliance
Architects' Tech Alliance
Dec 30, 2020 · Artificial Intelligence

Understanding GPUs, AI Accelerators, and Market Trends

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.

AI acceleratorGPUHPC
0 likes · 7 min read
Understanding GPUs, AI Accelerators, and Market Trends
Programmer DD
Programmer DD
Dec 17, 2020 · Artificial Intelligence

Can Huang’s Law Double AI Performance Every Two Years? NVIDIA GTC 2020 Insights

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.

AI performanceGPUHuang's Law
0 likes · 8 min read
Can Huang’s Law Double AI Performance Every Two Years? NVIDIA GTC 2020 Insights
Programmer DD
Programmer DD
Dec 6, 2020 · Cloud Native

Enable GPU Support in Kubernetes with Containerd and NVIDIA Runtime

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.

CUDADevice PluginsGPU
0 likes · 11 min read
Enable GPU Support in Kubernetes with Containerd and NVIDIA Runtime
Architects' Tech Alliance
Architects' Tech Alliance
Oct 28, 2020 · Artificial Intelligence

Understanding NVIDIA NVLink: Architecture, Features, and Applications

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.

GPU interconnectHigh-performance computingNVIDIA
0 likes · 7 min read
Understanding NVIDIA NVLink: Architecture, Features, and Applications
Programmer DD
Programmer DD
Sep 17, 2020 · Artificial Intelligence

Why Nvidia’s $40B Arm Acquisition Could Redefine AI Computing

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.

AIArmNVIDIA
0 likes · 8 min read
Why Nvidia’s $40B Arm Acquisition Could Redefine AI Computing
21CTO
21CTO
May 16, 2020 · R&D Management

How Nvidia’s Chief Scientist Built a $400 Open‑Source Ventilator to Fight COVID‑19

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.

Bill DallyCOVID-19NVIDIA
0 likes · 6 min read
How Nvidia’s Chief Scientist Built a $400 Open‑Source Ventilator to Fight COVID‑19
Architects' Tech Alliance
Architects' Tech Alliance
Dec 28, 2019 · Artificial Intelligence

Understanding CPU vs GPU, GPU Parameters, and NVIDIA Architectures for AI and High‑Performance Computing

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.

AICPUCUDA
0 likes · 13 min read
Understanding CPU vs GPU, GPU Parameters, and NVIDIA Architectures for AI and High‑Performance Computing
Architects' Tech Alliance
Architects' Tech Alliance
Feb 2, 2019 · Artificial Intelligence

An Overview of NVIDIA NVLink: Architecture, Topology, and Performance

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.

AI accelerationGPU interconnectNVIDIA
0 likes · 7 min read
An Overview of NVIDIA NVLink: Architecture, Topology, and Performance
Architects' Tech Alliance
Architects' Tech Alliance
Aug 9, 2017 · Fundamentals

Understanding NVIDIA GRID vGPU Virtualization and Its Allocation Modes

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.

Cloud ComputingGPU virtualizationGrid
0 likes · 10 min read
Understanding NVIDIA GRID vGPU Virtualization and Its Allocation Modes
Architects' Tech Alliance
Architects' Tech Alliance
Jul 2, 2017 · Fundamentals

Differences Between NVIDIA Tesla and GeForce GPUs: Architecture, Performance, and Use Cases

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.

GPUGPU architectureGeForce
0 likes · 11 min read
Differences Between NVIDIA Tesla and GeForce GPUs: Architecture, Performance, and Use Cases