This article breaks down how GPU compute capability, VRAM capacity, memory bandwidth, and inference frameworks each determine whether a graphics card can run large language models and how fast, using a factory analogy to show why raw specs alone are misleading.
The article explains that CPUs act as a few powerful “professors” optimized for low‑latency single tasks, while GPUs are thousands of simple “students” built for high‑throughput parallel workloads, making GPUs ideal for cryptocurrency mining and large‑model training.
This article breaks down NVIDIA GPU fundamentals—contrasting GPU with CPU design, tracing CUDA’s evolution, detailing the hardware hierarchy from chips to SM units, explaining memory tiers, and presenting a step‑by‑step performance‑optimization methodology for AI training and inference 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 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.
The article explains that GPUs, with thousands of simple cores and high‑bandwidth memory, excel at the massive parallel matrix computations required by large‑scale language model training, while CPUs handle control and scheduling, making the two processors complementary for AI workloads.
The article reviews two recent ISCA 2025 papers—FAST and Neo—that introduce hardware and GPU‑based accelerators employing hoisting, KLSS, and Tensor Core optimizations to significantly boost the performance of fully homomorphic encryption workloads.
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 traces NVIDIA’s GPU architecture evolution from the Volta era’s pioneering Tensor Cores through Turing, Ampere, Hopper, and the latest Blackwell and Rubin designs, highlighting key innovations such as mixed‑precision support, sparsity, NVLink, and their impact on deep‑learning performance.
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.
This article explains the fundamental performance indicators of AI chips—TOPS, TFLOPS, and precision formats like FP16, FP32, and INT8—while comparing GPU, ASIC, and TPU architectures, highlighting Tensor Core advantages and TPU's superior efficiency over CPUs and GPUs.
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.
The article surveys the AI compute ecosystem, explaining why CPUs are unsuitable for AI workloads, how heterogeneous CPU‑plus‑accelerator designs dominate, and detailing the evolution of NVIDIA GPUs, Tensor Cores, memory technologies, and inter‑GPU networking that enable large‑scale model training.
This article systematically examines the major performance bottlenecks in AI model training, explains the underlying hardware and software causes, and presents a comprehensive set of acceleration strategies—including data‑loading optimizations, compute‑side enhancements, communication tricks, and the AIAK‑Training suite—backed by real‑world case studies and quantitative results.
This article systematically analyzes the main performance bottlenecks in AI model training, explains why acceleration is essential, and presents current hardware‑ and software‑based solutions—including data‑loading optimizations, operator fusion, mixed‑precision and Tensor Core usage, as well as distributed communication strategies—followed by real‑world case studies of Baidu's AIAK‑Training suite that demonstrate significant speed‑ups.
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 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.
