This article explains the fundamentals of CUDA performance tuning, covering GPU architectures from Kepler to Volta, the role of SMX, warp schedulers, registers and memory hierarchies, and provides practical guidance on launch configuration, latency hiding, and thread‑block sizing to maximize throughput.
Nvidia’s latest GTC briefing details the Blackwell and Rubin GPU roadmaps, highlighting massive GPU shipments, new NVLink 6.0 interconnects, 448 Gbps SerDes, and architectural innovations aimed at boosting AI compute performance, efficiency, and scalability across data‑center workloads.
This article traces the evolution of Nvidia’s GPU architectures—from the early Tesla series through Fermi, Kepler, Maxwell, Pascal, Volta, Turing, Ampere, and the latest Hopper—detailing their specifications, key features, and offering a systematic decision‑making guide for AI server designers to select the optimal GPU based on workload, model size, precision, scalability, and total cost of ownership.
This article explains why GPUs have become the preferred platform for high‑performance computing, covering Dennard scaling, GPU speed advantages, theoretical FLOPS calculations, CUDA programming examples like SAXPY, the SIMT execution model, instruction pipelines, and modern techniques for handling branch divergence and register bank conflicts.
Nvidia's Rubin CPX GPU, unveiled in September 2025, uses GDDR7 memory and a split‑stage architecture to dramatically boost token‑per‑second rates for long‑context inference, while its integration into third‑generation Oberon servers promises higher power density, improved ROI, and scalable data‑center deployments.
The article analyzes Nvidia's 2023 Blackwell GPU series and GB200 NVL72 architecture, detailing their advanced 3‑4nm manufacturing, redesigned CUDA cores, next‑gen ray‑tracing and DLSS upgrades, massive compute and memory bandwidth gains, NVLink Gen5 improvements, and the diverse GB200 product configurations for high‑performance AI workloads.
Fermi, NVIDIA’s 2010 GPU architecture, introduced major upgrades over the Tesla line—including a 40 nm process, vastly increased transistor count, GDDR5 memory, L2 cache, enhanced FP64 performance, ECC support, and unified CPU‑GPU addressing—making it the first truly complete GPU computing platform.
This article explains why pre‑training large AI models consumes 90‑99% of total compute, describes the full training and inference pipelines, introduces resource‑saving strategies such as PD‑separation, and reviews market trends and infrastructure challenges shaping the next generation of AI systems.
This article traces the evolution of NVIDIA's GPU architectures—from Volta's pioneering Tensor Cores through Turing, Ampere, Hopper, and the latest Blackwell—highlighting key innovations such as mixed‑precision support, NVLink, and specialized Tensor Core designs that have dramatically boosted AI training and inference performance.
This article explains how NVIDIA transitioned from gaming graphics cards to general‑purpose GPUs with the first data‑center Tesla GPU, detailing the unified shader architecture, the internal components of TPCs and SMs, CUDA 1.0 programming basics, and performance calculations that illustrate the massive computational advantage over contemporary CPUs.
This article examines Nvidia's massive GB202 Blackwell GPU—its 750 mm² die, 922 billion transistors, 192 SMs, and extensive memory subsystem—while comparing its compute units, instruction caches, atomics, and bandwidth against AMD's RDNA4‑based RX 9070, highlighting architectural trade‑offs, performance metrics, and future GPU competition.
This article provides a detailed technical comparison between Nvidia's Blackwell GB202 GPU and AMD's RDNA4 RX 9070, covering CPU and GPU updates, SM front‑end design, memory hierarchies, execution units, atomics, L2 performance, power consumption, and real‑world benchmark results such as FluidX3D.
In March 2024 Nvidia unveiled the Blackwell GPU family and the GB200 NVL72 architecture, featuring 3‑4 nm processes, redesigned CUDA cores, next‑gen ray‑tracing, upgraded DLSS, massive FP16/FP8 compute gains, 8 TB/s memory bandwidth, and NVLink Gen5, while also presenting complex power, cooling, and packaging challenges for large‑scale AI deployments.
The article reviews NVIDIA's GPU architecture progression—from Volta's pioneering Tensor Cores through Turing, Ampere, Hopper, and the latest Blackwell and Rubin designs—highlighting key innovations, performance gains for deep learning, and related resource updates for AI practitioners.
The article explains CUDA’s architecture and SM basics, describes how the warp scheduler and dispatch units assign thread blocks to SMs, and concludes that external control cannot target a specific SM, while mentioning the NanoFlow intra‑device parallelism approach as a possible indirect optimization.
This article provides a detailed technical analysis of multi‑GPU server architectures, covering component breakdowns, NVSwitch networking, bandwidth calculations, and the differences between NVIDIA A100, A800, and H100 configurations for large‑scale AI workloads.
This article provides a comprehensive breakdown of high‑performance GPU server infrastructure, covering PCIe generations, NVLink evolution, NVSwitch and NVLink switches, HBM memory technologies, and bandwidth measurement units, helping readers understand the hardware connections and performance considerations essential for large‑scale model training.
The article analyses the historical evolution of Nvidia's NVLink and NVLink C2C interconnect technologies, compares them with PCIe, Ethernet and InfiniBand, and uses these trends to predict future AI‑chip architectures such as the B100 and X100 GPUs, highlighting design trade‑offs and packaging challenges.
This article provides an in‑depth technical analysis of AMD’s RDNA 2 GPU architecture, comparing its compute units, cache hierarchy, latency and bandwidth characteristics with Nvidia’s Ampere, and evaluates real‑world game performance in titles such as Cyberpunk 2077, Titanic Honor & Glory, and Gunner HEAT PC.
The article provides a detailed technical analysis of NVIDIA's Hopper‑based H100 GPU, covering its 4 nm process, 800 billion transistors, GPC/TPC hierarchy, new FP8 Tensor Cores, Transformer engine, Tensor Memory Accelerator, and the resulting six‑fold performance jump over the previous A100 generation.
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.
This article provides a concise overview of GPU computing fundamentals, covering GPU hardware components, memory hierarchy, parallel execution models, and the CUDA programming framework, illustrating how CPUs and GPUs cooperate in heterogeneous computing environments.
This article traces the evolution of GPUs from early graphics chips to modern parallel processors, explains their internal pipeline, compares CPU and GPU architectures, and introduces key acceleration frameworks like CUDA and OpenCL for general‑purpose computing.
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.
