After installing drivers on an NVIDIA Tesla A100, the GPU reports a constant 25% utilization despite no workload, which can be resolved by enabling persistence mode using a simple nvidia‑smi command to keep the driver loaded and improve performance stability.
This guide explains why Java architects should understand CUDA, describes the GPU programming model, compares CPU and GPU designs, and details three practical ways—JNI, JCuda, and TornadoVM—to integrate CUDA acceleration into Java applications, with tips for using A100 GPUs effectively.
This article analyzes NVIDIA's A100, H100, A800, H800, and H20 GPUs, compares their architectures, performance, and pricing, and provides a step‑by‑step guide for building a private AI compute center tailored to training, inference, and high‑performance computing workloads.
This step‑by‑step tutorial shows how to set up CUDA 12.4, install required packages, prepare a JSON dataset and custom reward, troubleshoot out‑of‑memory errors, and launch DeepSeek R1 training on an 8‑GPU A100 cluster using Accelerate, Deepspeed zero‑3 and vLLM configurations.
This article explains Nvidia's Multi‑Instance GPU (MIG) technology, compares it with vGPU, walks through enabling and partitioning MIG on A100 cards using nvidia‑smi commands, and shows how to expose MIG resources in Kubernetes with single and mixed strategies.
This article provides a detailed technical breakdown of modern multi‑GPU server nodes, covering component composition, storage network cards, NVSwitch interconnects, bandwidth calculations, and the architectural differences between NVIDIA A100/A800 and H100/H800 configurations for AI training workloads.
