BestHub
Sign in
Tagged articles

GPU

587 articles · Page 3 of 6
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Mar 9, 2025 · Cloud Computing

Deploy QwQ-32B LLM Inference on Alibaba Cloud ACS with vLLM: Step‑by‑Step Guide

This guide walks you through using Alibaba Cloud Container Compute Service (ACS) to provision GPU resources, prepare the QwQ-32B model, configure persistent storage, deploy the model with vLLM, set up OpenWebUI, verify the service, and optionally benchmark its performance, all with detailed commands and YAML examples.

ACSAlibaba CloudGPU
0 likes · 17 min read
Deploy QwQ-32B LLM Inference on Alibaba Cloud ACS with vLLM: Step‑by‑Step Guide
Infra Learning Club
Infra Learning Club
Mar 9, 2025 · Cloud Native

How to Fix nvidia-smi Missing GPU Process Info Inside Containers

The article explains why nvidia-smi cannot display GPU processes when run inside a container, analyzes the underlying pid‑namespace isolation and kernel‑level restrictions, and provides three practical solutions—including using hostPid, custom kernel interception modules, and the nvitop tool—plus a workaround for gpu‑operator deployments.

GPUHelmKernel Module
0 likes · 8 min read
How to Fix nvidia-smi Missing GPU Process Info Inside Containers
Infra Learning Club
Infra Learning Club
Mar 6, 2025 · Fundamentals

How GPU DVFS Boosts Efficiency: Concepts, Modeling, and Future Directions

This article explains how GPU Dynamic Voltage and Frequency Scaling (DVFS) reduces power consumption while preserving performance, describes NVIDIA GPU Boost 4.0 features, outlines a hardware‑counter‑based GPGPU power‑estimation model built with a BP‑ANN, reports sub‑5% error on benchmarks, and discusses intelligent and multi‑GPU extensions.

BP-ANNDVFSGPGPU
0 likes · 5 min read
How GPU DVFS Boosts Efficiency: Concepts, Modeling, and Future Directions
Baidu Geek Talk
Baidu Geek Talk
Mar 5, 2025 · Cloud Computing

Inside GPU Cloud Servers: Architecture, Interconnects, and Performance Secrets

This article provides a comprehensive technical overview of GPU cloud server design, covering data‑processing pipelines, hardware topology, NUMA considerations, PCIe and proprietary interconnects, multi‑GPU communication strategies, virtualization approaches (BCC and BBC), DPU acceleration, and future trends for scaling up and out.

Cloud ComputingGPUVirtualization
0 likes · 27 min read
Inside GPU Cloud Servers: Architecture, Interconnects, and Performance Secrets
JD Retail Technology
JD Retail Technology
Mar 4, 2025 · Artificial Intelligence

JD Retail End-to-End AI Engine Compatible with GPU and Domestic NPU: Architecture, Optimization, and Applications

JD Retail’s Nine‑Number Algorithm Platform delivers an end‑to‑end AI engine that unifies GPU and domestic NPU resources across a thousand‑card cluster, offering zero‑cost model migration, optimized training and inference pipelines, support for over 40 LLM and multimodal models, and proven business‑level performance that reduces dependence on overseas chips.

AIDistributed TrainingGPU
0 likes · 19 min read
JD Retail End-to-End AI Engine Compatible with GPU and Domestic NPU: Architecture, Optimization, and Applications
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Mar 3, 2025 · Cloud Computing

How Baidu Cloud Optimizes GPU Servers for AI Workloads

This article explains the design and implementation of GPU cloud servers, covering data processing pipelines, hardware selection, topology, interconnect technologies, virtualization, multi‑GPU communication methods, and Baidu's practical solutions for both virtualized and bare‑metal instances to boost AI inference and training performance.

AICloud ComputingGPU
0 likes · 29 min read
How Baidu Cloud Optimizes GPU Servers for AI Workloads
JD Tech Talk
JD Tech Talk
Mar 3, 2025 · Artificial Intelligence

AI Engine Technology Based on Domestic Chips for JD Retail

This article describes JD Retail's AI engine built on domestic NPU chips, covering challenges, heterogeneous GPU‑NPU scheduling, high‑performance training and inference engines, extensive model support, real‑world deployment cases, and future plans for large‑scale chip clusters and ecosystem development.

AIDistributed TrainingGPU
0 likes · 20 min read
AI Engine Technology Based on Domestic Chips for JD Retail
IT Services Circle
IT Services Circle
Feb 27, 2025 · Artificial Intelligence

DeepSeek Announces FlashMLA: An Efficient Multi‑Layer Attention Decoding Kernel for Hopper GPUs

DeepSeek’s OpenSourceWeek introduced FlashMLA, a GPU‑optimized MLA decoding kernel for Hopper GPUs that leverages FlashAttention and CUTLASS to dramatically improve large‑model inference performance, with early adoption showing up to 30% higher compute utilization and doubled speed in some scenarios.

Artificial IntelligenceDeepSeekFlashMLA
0 likes · 3 min read
DeepSeek Announces FlashMLA: An Efficient Multi‑Layer Attention Decoding Kernel for Hopper GPUs
JavaEdge
JavaEdge
Feb 24, 2025 · Artificial Intelligence

Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide

This tutorial walks Java developers through building, training, evaluating, and deploying a CIFAR‑10 image classifier using PyTorch, covering data loading, preprocessing, network definition, loss and optimizer setup, GPU acceleration, model saving, and per‑class accuracy analysis.

CIFAR-10GPUPyTorch
0 likes · 18 min read
Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Feb 24, 2025 · Artificial Intelligence

Unlock Data+AI Fusion: Fine‑Tune Multimodal Models on DataWorks with GPU‑Ready Notebooks

This tutorial shows how to use Alibaba Cloud DataWorks' serverless GPU resource groups together with the open‑source LLaMA‑Factory framework to fine‑tune the Qwen2‑VL‑2B multimodal model for tourism‑domain Q&A, covering environment setup, dataset preparation, parameter configuration, training, and interactive inference.

DataWorksGPULLaMA-Factory
0 likes · 10 min read
Unlock Data+AI Fusion: Fine‑Tune Multimodal Models on DataWorks with GPU‑Ready Notebooks
Infra Learning Club
Infra Learning Club
Feb 23, 2025 · Fundamentals

How to Dynamically Decompress CUDA Fatbin Files Compressed by NVCC

This article explains why enabling NVCC's --fatbin-options -compress-all breaks remote GPU calls, describes the fatbin file layout, shows how to extract and analyze the binary with objcopy, and provides a step‑by‑step implementation of a decompression routine for both ELF and PTX sections.

Binary FormatCUDAGPU
0 likes · 9 min read
How to Dynamically Decompress CUDA Fatbin Files Compressed by NVCC
Infra Learning Club
Infra Learning Club
Feb 22, 2025 · Fundamentals

Understanding NVCC Compilation: A Step‑by‑Step Technical Guide

This article walks through the NVCC compilation pipeline, explaining how CUDA source files are transformed into host and device binaries, detailing file extensions, compilation stages, command‑line options, intermediate artifacts, and the role of registration functions such as __nv_cudaEntityRegisterCallback and __sti____cudaRegisterAll.

CUDACompilationGCC
0 likes · 12 min read
Understanding NVCC Compilation: A Step‑by‑Step Technical Guide
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Feb 21, 2025 · Artificial Intelligence

Deploying DeepSeek R1 Model Inference on ACK Edge with Virtual Nodes and Serverless GPU

This article explains how to use Alibaba Cloud ACK Edge to manage on‑premise GPU resources and seamlessly fall back to cloud‑based ACS Serverless GPU via virtual nodes for deploying DeepSeek R1 inference, covering environment preparation, model download, storage setup, custom scheduling, and scaling strategies.

ACK@EdgeDeepSeekGPU
0 likes · 16 min read
Deploying DeepSeek R1 Model Inference on ACK Edge with Virtual Nodes and Serverless GPU
Python Programming Learning Circle
Python Programming Learning Circle
Feb 18, 2025 · Artificial Intelligence

Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects

This article introduces PyTorch, covering installation on CPU/GPU, basic tensor operations, automatic differentiation, building and training neural networks, data loading with DataLoader, image classification on MNIST, model deployment, and useful tips for accelerating deep‑learning workflows.

GPUPyTorchdeep learning
0 likes · 9 min read
Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects
Infra Learning Club
Infra Learning Club
Feb 15, 2025 · Cloud Native

Advanced Guide: Real‑Time GPU Process Migration in Kubernetes with CRIU

This article explains how os‑criu provides transparent, OS‑level GPU checkpoint/restore, compares its performance with NVIDIA's cuda‑checkpoint, walks through building and installing the PhOS framework, demonstrates migration of a Llama2‑13b‑chat workload in Docker, and discusses current limitations and future Kubernetes integration plans.

CRIUCheckpointDocker
0 likes · 9 min read
Advanced Guide: Real‑Time GPU Process Migration in Kubernetes with CRIU
Ops Development & AI Practice
Ops Development & AI Practice
Feb 15, 2025 · Artificial Intelligence

How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth

This article provides a step‑by‑step, code‑rich tutorial for fine‑tuning the open‑source Llama 3 1B and 3B models on Google Colab using the Unsloth library and LoRA, covering environment setup, model loading, adapter insertion, dataset preparation, training configuration, inference, and model saving, all while keeping GPU memory usage low.

AIColabFine-tuning
0 likes · 13 min read
How to Efficiently Fine‑Tune Llama 3 on a Free Colab T4 GPU with Unsloth
Code Mala Tang
Code Mala Tang
Feb 10, 2025 · Artificial Intelligence

How Much Does It Really Cost to Run a Full‑Scale DeepSeek AI Locally?

This article breaks down the hardware and software expenses required to deploy a complete DeepSeek large‑language model on‑premises, revealing a total cost of roughly $110,000 and explaining why such an investment is prohibitive for most individual developers but may be justified for well‑funded research or corporate projects.

DeepSeekGPUcost analysis
0 likes · 4 min read
How Much Does It Really Cost to Run a Full‑Scale DeepSeek AI Locally?
21CTO
21CTO
Feb 8, 2025 · Artificial Intelligence

Can Java Overtake Python in AI? Insights from the 2025 Azul Report

A recent Azul Systems study suggests that Java may surpass Python in enterprise AI development within the next 18‑36 months, highlighting Java's scalability, performance, and emerging GPU projects while acknowledging cultural and tooling advantages that still favor Python.

AIDevOpsGPU
0 likes · 9 min read
Can Java Overtake Python in AI? Insights from the 2025 Azul Report
Open Source Linux
Open Source Linux
Feb 7, 2025 · Operations

China's Xinchang Server Ecosystem: Market Trends, Key Players, and Future Risks

This article provides a comprehensive analysis of China's Xinchang server industry, covering the upstream component supply chain, mid‑stream manufacturers, downstream users, shipment statistics, market share evolution, competitive tiers, application demands, and the technical and ecological challenges facing domestic CPU and GPU development.

CPUChinese hardwareGPU
0 likes · 10 min read
China's Xinchang Server Ecosystem: Market Trends, Key Players, and Future Risks
Top Architect
Top Architect
Feb 6, 2025 · Artificial Intelligence

Deploying DeepSeek R1 671B Model Locally with Ollama: Quantization, Hardware Requirements, and Step‑by‑Step Guide

This article provides a comprehensive tutorial on locally deploying the full‑size DeepSeek R1 671B model using Ollama, covering dynamic quantization options, hardware specifications, detailed installation commands, configuration files, performance observations, and practical recommendations for consumer‑grade systems.

AIDeepSeekGPU
0 likes · 14 min read
Deploying DeepSeek R1 671B Model Locally with Ollama: Quantization, Hardware Requirements, and Step‑by‑Step Guide
AI Cyberspace
AI Cyberspace
Feb 5, 2025 · Fundamentals

From 2D Cards to AI Powerhouses: The Evolution of GPUs

This article traces the GPU's journey from early 2D graphics cards to modern GPGPUs powering AI and HPC, explains core hardware components, compares GPU and CPU architectures, and details the 3D rendering pipeline that underlies graphics and parallel computation.

GPUGraphics Processing Unitcomputer architecture
0 likes · 10 min read
From 2D Cards to AI Powerhouses: The Evolution of GPUs
Code Mala Tang
Code Mala Tang
Feb 2, 2025 · Artificial Intelligence

How to Deploy DeepSeek AI Coding Assistant Locally: A Step‑by‑Step Guide

This guide walks you through the hardware and software prerequisites, Docker-based installation, environment configuration, model fine‑tuning, IDE integration, maintenance, and troubleshooting for running the DeepSeek AI programming assistant entirely on your own machine.

AI coding assistantDeepSeekDocker
0 likes · 12 min read
How to Deploy DeepSeek AI Coding Assistant Locally: A Step‑by‑Step Guide
Infra Learning Club
Infra Learning Club
Jan 24, 2025 · Fundamentals

Inside NVCC: How CUDA Code Is Compiled and Linked

The article dissects NVCC’s compilation pipeline, showing how internal registration functions from host_runtime.h are injected into the host binary, how a simple CUDA demo is processed with --dryrun, and how the generated fatbin, PTX, and cubin files are linked and registered for GPU execution.

CUDACompilationFatBinary
0 likes · 10 min read
Inside NVCC: How CUDA Code Is Compiled and Linked
Architects' Tech Alliance
Architects' Tech Alliance
Jan 23, 2025 · Game Development

GPU Architecture and Rendering Pipeline Overview

This article provides a comprehensive overview of modern GPU architecture, covering components such as SMs, GPCs, memory hierarchy, unified shader architecture, SIMT execution, warp scheduling, and compares IMR, TBR, and TBDR rendering pipelines while offering practical optimization techniques for developers.

GPUGraphicsOptimization
0 likes · 27 min read
GPU Architecture and Rendering Pipeline Overview
Python Programming Learning Circle
Python Programming Learning Circle
Jan 15, 2025 · Fundamentals

Python Performance Optimization Tools and Libraries

This article introduces a comprehensive set of Python performance‑enhancing tools and libraries—including NumPy, SciPy, PyPy, Cython, Numba, GPU‑based solutions, and various wrappers—explaining how they accelerate code execution, reduce memory usage, and enable efficient single‑ and multi‑processor programming.

CompilationGPULibraries
0 likes · 8 min read
Python Performance Optimization Tools and Libraries
Architects' Tech Alliance
Architects' Tech Alliance
Jan 14, 2025 · Industry Insights

AI Server Market 2024: Growth Trends, Types, and Key Challenges

The 2024 AI server market is booming with global shipments surpassing 1.2 million units in 2023 and projected to reach 1.67 million in 2024, driven by rapid growth in China’s AI compute capacity, distinct training and inference server designs, and facing challenges in GPU quality, high‑speed interconnects, and cooling solutions.

2024AI hardwareAI servers
0 likes · 5 min read
AI Server Market 2024: Growth Trends, Types, and Key Challenges
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Jan 14, 2025 · Cloud Native

Managing Distributed ECS Resources with ACK Edge and Kubernetes

This guide explains how to use Alibaba Cloud's ACK Edge to create a secure, high‑availability Kubernetes cluster that unifies management and scheduling of ECS instances across multiple VPCs, regions, and accounts, with detailed scenarios, advantages, step‑by‑step procedures, and sample YAML deployments.

ACK@EdgeDaemonSetDistributed Resources
0 likes · 8 min read
Managing Distributed ECS Resources with ACK Edge and Kubernetes
Java Tech Enthusiast
Java Tech Enthusiast
Jan 9, 2025 · Cloud Native

Configuring NVIDIA Docker Plugin and GPU Access in Kubernetes

This guide walks through installing the NVIDIA container toolkit, configuring Docker to use the NVIDIA runtime, verifying GPU access, deploying the NVIDIA device plugin in Kubernetes, labeling GPU nodes, and running a GPU‑accelerated FFmpeg pod to confirm successful GPU integration.

Container ToolkitDockerGPU
0 likes · 12 min read
Configuring NVIDIA Docker Plugin and GPU Access in Kubernetes
Liangxu Linux
Liangxu Linux
Jan 8, 2025 · Cloud Native

Enable NVIDIA GPU Access in Docker and Kubernetes with the NVIDIA Container Toolkit

This guide walks through checking system and software environments, installing and configuring the NVIDIA Docker plugin, verifying GPU access in Docker containers, deploying the NVIDIA device plugin on a Kubernetes cluster, creating GPU‑enabled pods, and troubleshooting common issues, all with concrete commands and configuration examples.

Container ToolkitFFmpegGPU
0 likes · 12 min read
Enable NVIDIA GPU Access in Docker and Kubernetes with the NVIDIA Container Toolkit
21CTO
21CTO
Jan 7, 2025 · Artificial Intelligence

Nvidia Reveals RTX 50 GPUs, Thor Auto Chip, and AI Supercomputer at CES 2025

At CES 2025, Nvidia CEO Jensen Huang announced the RTX 50 series GPUs built on the Blackwell architecture, the Thor automotive processor, the Project Digits personal AI supercomputer, new AI agents and robotics initiatives, detailing pricing, performance specs, and partnerships across automotive and AI ecosystems.

Artificial IntelligenceAutomotiveCES 2025
0 likes · 10 min read
Nvidia Reveals RTX 50 GPUs, Thor Auto Chip, and AI Supercomputer at CES 2025
Architects' Tech Alliance
Architects' Tech Alliance
Jan 6, 2025 · Industry Insights

How Nvidia’s GB300 GPU Is Shaping AI Inference and Cloud Supply Chains

The article provides a detailed technical analysis of Nvidia’s new GB300 and B300 GPUs, comparing their performance, memory architecture, and power consumption to previous generations, and examines how these changes affect AI inference workloads, NVL72 accelerator systems, and the supply‑chain strategies of major cloud providers.

AI InferenceCloud ComputingGPU
0 likes · 12 min read
How Nvidia’s GB300 GPU Is Shaping AI Inference and Cloud Supply Chains
Infra Learning Club
Infra Learning Club
Jan 4, 2025 · Cloud Native

How GPU Devices Are Dynamically Mounted to Kubernetes Pods

This article dissects the GPUMounter project's implementation of dynamic GPU device mounting to a pod, detailing the roles of cgroups (v1 and v2) and Linux namespaces, and provides step‑by‑step command‑line examples and a CLI tool for practical use.

GPUKubernetesNamespace
0 likes · 13 min read
How GPU Devices Are Dynamically Mounted to Kubernetes Pods
Architects' Tech Alliance
Architects' Tech Alliance
Dec 29, 2024 · Industry Insights

Why Broadcom’s $1T Valuation Signals a New Era for AI ASICs

Broadcom’s market‑cap breakthrough past $1 trillion highlights its strategic push into AI ASICs, revealing how ASIC‑FPGA trade‑offs, collaborations with Google, and competition with Nvidia’s GPU ecosystem are reshaping the high‑performance computing landscape.

AI ASICBroadcomChip Design
0 likes · 13 min read
Why Broadcom’s $1T Valuation Signals a New Era for AI ASICs
DataFunSummit
DataFunSummit
Dec 28, 2024 · Artificial Intelligence

Memory Optimization for Large Model Inference: Virtual Tensor and LayerKV Techniques

This talk presents the Ant Group team's recent work on large‑model inference memory optimization, covering GPU memory challenges, virtual memory management (VMM), the Virtual Tensor framework, LayerKV techniques, performance comparisons with Page Attention and FlashAttention, and extensive experimental results demonstrating reduced latency and higher QPS.

GPULarge Model InferenceVirtual Memory
0 likes · 25 min read
Memory Optimization for Large Model Inference: Virtual Tensor and LayerKV Techniques
Architects' Tech Alliance
Architects' Tech Alliance
Dec 6, 2024 · Industry Insights

How GPU Virtualization Works: Layers, Techniques, and Real-World Use Cases

This article explains the fundamentals of GPU architecture, the need for GPU virtualization, and walks through user‑level, kernel‑level, hardware‑level, and full GPU virtualization techniques, illustrating each layer with diagrams and code examples while highlighting practical deployment scenarios.

Cloud InfrastructureGPUHardware Acceleration
0 likes · 10 min read
How GPU Virtualization Works: Layers, Techniques, and Real-World Use Cases
Baidu Tech Salon
Baidu Tech Salon
Nov 22, 2024 · Artificial Intelligence

How GPU‑Accelerated ANN Search Cuts Costs and Boosts Throughput in High‑Volume Retrieval

This article analyzes a GPU‑based approximate nearest neighbor (ANN) retrieval solution built on NVIDIA's RAFT library, detailing algorithm selection, offline indexing tricks, batch online search design, performance results on a 25‑million‑vector workload, and cost‑saving implications for large‑scale search services.

ANNGPUIVF_INT8
0 likes · 21 min read
How GPU‑Accelerated ANN Search Cuts Costs and Boosts Throughput in High‑Volume Retrieval
Architects' Tech Alliance
Architects' Tech Alliance
Nov 21, 2024 · Fundamentals

El Capitan Supercomputer and the Rise of AMD GPU‑Driven HPC: Architecture, Performance, and Market Impact

The article examines the El Capitan supercomputer unveiled at SC24, detailing its AMD CPU‑GPU hybrid architecture, benchmark results, its dominance in the November 2024 Top500 list, and the broader implications for high‑performance computing, AI workloads, and the competitive landscape between AMD and NVIDIA.

AIAMDCPU
0 likes · 20 min read
El Capitan Supercomputer and the Rise of AMD GPU‑Driven HPC: Architecture, Performance, and Market Impact
Baidu Geek Talk
Baidu Geek Talk
Nov 20, 2024 · Artificial Intelligence

Boosting ANN Search with GPU: Inside RAFT’s IVF_INT8 Implementation

This article examines how Baidu and NVIDIA leveraged the open‑source RAFT library to build a GPU‑accelerated approximate nearest neighbor (ANN) retrieval system, detailing algorithm choices, offline indexing, online batch processing, performance results, and practical guidelines for deploying ANN on GPUs.

ANNGPUIVF_INT8
0 likes · 20 min read
Boosting ANN Search with GPU: Inside RAFT’s IVF_INT8 Implementation
AntTech
AntTech
Nov 16, 2024 · Information Security

WarpDrive: GPU-Based Fully Homomorphic Encryption Acceleration Leveraging Tensor and CUDA Cores Accepted at HPCA 2025

Ant Group’s Computing Systems Lab announced that its GPU‑accelerated fully homomorphic encryption framework WarpDrive, which exploits Tensor and CUDA cores for high‑throughput NTT operations and parallel kernel designs, has been accepted as a paper at the IEEE HPCA 2025 conference.

CUDAFully Homomorphic EncryptionGPU
0 likes · 4 min read
WarpDrive: GPU-Based Fully Homomorphic Encryption Acceleration Leveraging Tensor and CUDA Cores Accepted at HPCA 2025
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Nov 13, 2024 · Industry Insights

Why GPU Scale‑Up Interconnects Need a New Protocol – Inside UALink and Alibaba’s Alink

The article analyzes the growing demand for high‑bandwidth, low‑latency GPU Scale‑Up interconnects in AI clusters, explains why existing Ethernet and RDMA solutions fall short, and examines the industry‑wide UALink alliance and Alibaba's Alink System as a new open‑ecosystem solution.

AI infrastructureAlink SystemCloud Computing
0 likes · 12 min read
Why GPU Scale‑Up Interconnects Need a New Protocol – Inside UALink and Alibaba’s Alink
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Nov 7, 2024 · Artificial Intelligence

RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search

RTAMS‑GANNS, the award‑winning real‑time adaptive multi‑stream GPU system for online approximate nearest neighbor search, eliminates costly memory allocations and serial execution by using a dynamic memory‑block insertion algorithm and separate CUDA streams, cutting latency by 40‑80% and reliably serving over 100 million daily users in production.

Approximate Nearest NeighborGPUHigh QPS
0 likes · 19 min read
RTAMS-GANNS: A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighbor Search
Linux Kernel Journey
Linux Kernel Journey
Nov 5, 2024 · Artificial Intelligence

Understanding AI Flame Graphs: Insights from Brendan Gregg

The article introduces Intel's AI Flame Graph, a low‑overhead profiling tool that visualizes AI accelerator and GPU workloads across the full software stack, explains its design, demonstrates SYCL matrix‑multiply benchmarks, discusses challenges of AI instruction analysis, and outlines future adoption and impact.

AI profilingGPUIntel
0 likes · 16 min read
Understanding AI Flame Graphs: Insights from Brendan Gregg
Linux Code Review Hub
Linux Code Review Hub
Nov 2, 2024 · Artificial Intelligence

Inside Intel’s AI Flame Graph: Low‑Overhead Profiling for Faster, Greener AI

The article introduces Intel’s AI Flame Graph, a low‑overhead profiling tool that visualizes AI accelerator and GPU execution alongside the full software stack, explains its design, shows SYCL matrix‑multiply examples, discusses challenges of AI workload analysis, and outlines future adoption and impact on performance and energy savings.

AI profilingGPUIntel
0 likes · 16 min read
Inside Intel’s AI Flame Graph: Low‑Overhead Profiling for Faster, Greener AI
Architecture and Beyond
Architecture and Beyond
Nov 2, 2024 · Artificial Intelligence

Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI

This tutorial walks programmers through preparing a GPU cloud environment, installing ComfyUI, downloading Flux1_dev models, integrating a custom LoRA, labeling generated images, and finally training the LoRA using ai‑toolkit, providing detailed commands, configuration tips, and practical cost estimates.

AI image generationComfyUIFlux
0 likes · 12 min read
Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI
Kuaishou Tech
Kuaishou Tech
Oct 16, 2024 · Frontend Development

How Kola2d’s WebGL Engine Achieves 50+ FPS for Million‑Cell Spreadsheets

This article details the design and optimization of Kola2d, a custom WebGL rendering engine for Docs online spreadsheets, explaining why WebGL was chosen, how the system separates business and rendering layers, and the many performance tricks that enable smooth 50+ FPS rendering of tables with up to a million cells.

GPUKola2dOnline Spreadsheet
0 likes · 19 min read
How Kola2d’s WebGL Engine Achieves 50+ FPS for Million‑Cell Spreadsheets
21CTO
21CTO
Oct 15, 2024 · Artificial Intelligence

Why Mojo Could Redefine AI Programming: Insights from Chris Lattner

The article explores Chris Lattner’s vision for Mojo—a Python‑compatible language designed for AI, GPU, and accelerator workloads—detailing its performance claims, SIMD support, complex‑number handling, and the growing developer community behind it.

AIGPUMojo
0 likes · 9 min read
Why Mojo Could Redefine AI Programming: Insights from Chris Lattner
Architects' Tech Alliance
Architects' Tech Alliance
Oct 7, 2024 · Industry Insights

What AMD Unveiled at Computex 2024: Zen 5, XDNA NPU, Ryzen 9000 and AI‑Focused Innovations

At Computex 2024, AMD showcased its latest CPU, GPU, and AI‑accelerated technologies—including the high‑performance Zen 5 core, second‑generation XDNA NPU with 50 TOPS, the Ryzen 9000 consumer processor, the AI‑PC Strix Point platform, Versal AI Edge Gen 2, the upcoming MI‑series AI GPUs, and the new UA‑Link interconnect—highlighting the company’s roadmap for next‑generation computing and AI workloads.

AIAMDCPU
0 likes · 5 min read
What AMD Unveiled at Computex 2024: Zen 5, XDNA NPU, Ryzen 9000 and AI‑Focused Innovations
Java Tech Enthusiast
Java Tech Enthusiast
Sep 30, 2024 · Artificial Intelligence

The AI Smile Curve: Profit Distribution and Future Outlook

The AI industry’s profit landscape mirrors a smile curve, with upstream GPU manufacturers and downstream application developers capturing most returns while costly large‑model R&D yields low margins, prompting predictions of GPU valuation corrections, a push for consumer‑facing killer apps, and massive application turnover through creative destruction.

AIGPUindustry analysis
0 likes · 11 min read
The AI Smile Curve: Profit Distribution and Future Outlook
Architects' Tech Alliance
Architects' Tech Alliance
Sep 29, 2024 · Industry Insights

Why Super‑Heterogeneous Computing Is the Next Frontier in Computing Architecture

The article analyzes the limits of the von Neumann model and Moore's law, explains how instruction set complexity defines processor categories, and argues that integrating CPUs, GPUs, FPGAs, DPUs and ASICs into a super‑heterogeneous ecosystem—driven by Intel, NVIDIA, ARM and emerging trends—will shape the future of computing through diverse workloads, AI demand, green efficiency and a global compute network by 2030.

AIARMCPU
0 likes · 12 min read
Why Super‑Heterogeneous Computing Is the Next Frontier in Computing Architecture
Architects' Tech Alliance
Architects' Tech Alliance
Sep 25, 2024 · Fundamentals

NVIDIA Quantum‑2 InfiniBand Platform: Technical Overview, Q&A, and Deployment Guidance

This article explains the growing demand for high‑performance computing, introduces NVIDIA's Quantum‑2 InfiniBand platform with its high‑speed, low‑latency capabilities, provides a curated list of related technical articles, and offers an extensive Q&A covering compatibility, cabling, UFM, PCIe limits, and best‑practice deployment for AI and HPC workloads.

AIGPUInfiniBand
0 likes · 11 min read
NVIDIA Quantum‑2 InfiniBand Platform: Technical Overview, Q&A, and Deployment Guidance
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Sep 18, 2024 · Artificial Intelligence

How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code

This article explains why single‑machine resources are insufficient for training ever‑larger language models, introduces the fundamentals of distributed training systems, details various parallel strategies such as data, model, pipeline, and hybrid parallelism, and provides practical PyTorch code and memory‑optimization techniques to accelerate large‑scale model training.

GPULarge Language ModelsPyTorch
0 likes · 29 min read
How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code
Infra Learning Club
Infra Learning Club
Sep 16, 2024 · Cloud Native

Survey of GPU Sharing and Virtualization Solutions for Kubernetes

The article surveys open‑source GPU sharing and virtualization approaches for AI workloads, comparing soft isolation, CUDA‑level isolation, NVIDIA MPS, driver‑level isolation, GPU pooling and deep‑learning memory sharing, and highlights their architectures, isolation guarantees, and performance trade‑offs.

Device PluginGPUKubernetes
0 likes · 5 min read
Survey of GPU Sharing and Virtualization Solutions for Kubernetes
Architects' Tech Alliance
Architects' Tech Alliance
Aug 25, 2024 · Industry Insights

Why GPUs May Lose the AI Race: TPU, FPGA, and Future Hardware Trends

While GPUs have driven AI acceleration for years, this article analyzes their architectural constraints, compares emerging alternatives such as Google's TPU and high‑end FPGAs, and explores future application niches like VR/AR, cloud gaming, and military systems where GPUs may still thrive or be replaced.

AI hardwareFPGAGPU
0 likes · 15 min read
Why GPUs May Lose the AI Race: TPU, FPGA, and Future Hardware Trends
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Aug 23, 2024 · Mobile Development

GPU Command and Syncpoint Analysis on SM8650 Platform

On the SM8650 platform, GLES issues synchronous and draw commands that the kernel‑mode driver translates into kgsl_drawobj structures, queues them in per‑context dispatch lists, processes fence, timestamp, and timeline syncpoints via dedicated kernel threads, and finally submits draw objects to the GPU firmware, with eglSwapBuffers triggering a fence syncpoint, a draw command, and a GPU fence creation.

AndroidGPUGraphics
0 likes · 12 min read
GPU Command and Syncpoint Analysis on SM8650 Platform
Baidu Geek Talk
Baidu Geek Talk
Aug 19, 2024 · Artificial Intelligence

PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains

The PaddlePaddle Neural Network Compiler (CINN) combines a PIR‑based frontend that performs graph‑level optimizations such as constant folding, dead‑code elimination and operator fusion with a backend that applies schedule transformations and auto‑tuning, delivering up to 4× faster RMSNorm kernels and 30‑60% overall speed‑ups for generative AI and scientific‑computing workloads.

CINNGPUOptimization
0 likes · 18 min read
PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains
ByteDance Cloud Native
ByteDance Cloud Native
Aug 12, 2024 · Cloud Native

How to Deploy NVIDIA NIM AI Models on Volcengine VKE in Minutes

This guide walks you through deploying large language models with NVIDIA NIM on Volcengine's Kubernetes Engine (VKE), covering environment setup, model optimization, Helm chart deployment, monitoring integration, and the key advantages of using NIM as a cloud‑native AI micro‑service.

AI deploymentGPUHelm
0 likes · 12 min read
How to Deploy NVIDIA NIM AI Models on Volcengine VKE in Minutes
Architects' Tech Alliance
Architects' Tech Alliance
Aug 8, 2024 · Artificial Intelligence

Fundamental Key Parameters of AI Chips: Compute Power, Precision Formats, and Architecture

This article explains the essential metrics of AI chips—including TOPS and TFLOPS compute, precision formats like FP16, FP32 and INT8, and the roles of GPUs, ASICs and TPUs—while highlighting how Tensor Cores boost deep‑learning performance and comparing TPU efficiency to CPUs and GPUs.

AI chipsASICArtificial Intelligence
0 likes · 4 min read
Fundamental Key Parameters of AI Chips: Compute Power, Precision Formats, and Architecture
Architects' Tech Alliance
Architects' Tech Alliance
Jul 25, 2024 · Artificial Intelligence

NVIDIA H20 AI Chip Launch and the Rapid Growth of China's AI Chip Market

The article reviews NVIDIA's newly released H20 AI accelerator for China, compares its performance and pricing with domestic chips, outlines the expanding Chinese AI chip ecosystem—including Huawei, Cambricon, HaiGuang, Alibaba, ByteDance, and Baidu—while highlighting market size growth, multi‑chip heterogeneity strategies, and the strong demand forecast through 2024.

AI chipsAI computeChina
0 likes · 8 min read
NVIDIA H20 AI Chip Launch and the Rapid Growth of China's AI Chip Market
360 Smart Cloud
360 Smart Cloud
Jul 17, 2024 · Artificial Intelligence

Parallelism and Memory‑Optimization Techniques for Distributed Large‑Scale Transformer Training

This article reviews the principles and practical implementations of data, pipeline, tensor, sequence, and context parallelism together with memory‑saving strategies such as recomputation and ZeRO, and demonstrates how the QLM framework leverages these techniques to accelerate large‑model training and fine‑tuning on multi‑GPU clusters.

GPUMegatron-LMMemory Optimization
0 likes · 18 min read
Parallelism and Memory‑Optimization Techniques for Distributed Large‑Scale Transformer Training
Architects' Tech Alliance
Architects' Tech Alliance
Jul 9, 2024 · Industry Insights

How Nvidia’s Accelerated GPU Roadmap Is Shaping AI‑Scale Networking

Nvidia plans to shorten its GPU generation cycle to one year, launching Blackwell Ultra in 2025, Rubin in 2026, and Rubin Ultra in 2027, while boosting token‑generation efficiency and introducing AI‑optimized Ethernet solutions like Spectrum‑X800, aiming to dominate large‑scale AI clusters and reshape the high‑performance networking market.

AIEthernetGPU
0 likes · 6 min read
How Nvidia’s Accelerated GPU Roadmap Is Shaping AI‑Scale Networking
Architects' Tech Alliance
Architects' Tech Alliance
Jun 22, 2024 · Artificial Intelligence

Rising Compute Demand of Generative AI Models and GPU Accelerator Trends in 2024

The article analyzes how generative AI models from GPT‑1 to the upcoming GPT‑5 are driving exponential growth in compute requirements, prompting massive cloud capital expenditures and intense competition among GPU vendors such as NVIDIA, AMD, Google, and emerging domestic chip makers, while also highlighting interconnect innovations and cost‑effective solutions.

AIAcceleratorsCompute
0 likes · 12 min read
Rising Compute Demand of Generative AI Models and GPU Accelerator Trends in 2024
Architects' Tech Alliance
Architects' Tech Alliance
Jun 16, 2024 · Industry Insights

How Nvidia’s Blackwell GPUs Aim to Slash AI Training Costs and Power

The article analyzes Nvidia’s historic advantage, the massive performance and energy efficiency gains from Pascal to Blackwell GPUs, the economics of training large language models like GPT‑4, and the detailed roadmap of upcoming GPU, memory, and interconnect technologies shaping the future of data‑center AI.

AIGPUNvidia
0 likes · 14 min read
How Nvidia’s Blackwell GPUs Aim to Slash AI Training Costs and Power
Java Tech Enthusiast
Java Tech Enthusiast
Jun 7, 2024 · Fundamentals

Engineer Builds GPU from Scratch in Two Weeks

In just two weeks, engineer Adam Majmudar designed and implemented a minimalist GPU called tiny‑gpu—complete with a custom 11‑instruction ISA, Verilog RTL, and verified via OpenLane—sharing the open‑source project on GitHub, earning thousands of stars, and preparing it for fabrication through Tiny Tapeout 7, showcasing how modern tools make DIY chip design increasingly accessible.

Chip DesignEDAGPU
0 likes · 8 min read
Engineer Builds GPU from Scratch in Two Weeks
IT Services Circle
IT Services Circle
Jun 6, 2024 · Artificial Intelligence

Nvidia Unveils Blackwell GPU and AI Supercomputing Roadmap

Nvidia’s latest Blackwell GPU, presented by Jensen Huang, promises unprecedented performance and energy efficiency for large‑scale AI models, while the company also showcases accelerated computing, NVLink interconnects, AI‑optimized DGX servers, the NIM platform for rapid LLM deployment, and ambitious projects such as Earth‑2 digital twins and next‑generation embodied AI robots.

AIBlackwellGPU
0 likes · 18 min read
Nvidia Unveils Blackwell GPU and AI Supercomputing Roadmap
Architects' Tech Alliance
Architects' Tech Alliance
Jun 5, 2024 · Industry Insights

How HBM Is Transforming GPU Power and Driving the AI Memory Boom

HBM's near‑memory architecture, stacked design, and TSV integration dramatically cut latency and space while boosting bandwidth, leading NVIDIA and AMD to adopt it across multiple GPU generations, spurring fierce competition among SK Hynix, Samsung, and Micron and projecting a four‑fold market surge to $169 billion by 2024.

AIGPUHBM
0 likes · 11 min read
How HBM Is Transforming GPU Power and Driving the AI Memory Boom