GPU Cloud Hosting

By Shivlendra Singh Jadoun
September 21, 2026

Multi-GPU Servers: Why NVLink Beats PCIe for Training

Quick Summary Most engineering teams do not misconfigure a Multi-GPU server on purpose. They buy several accelerators, plug them into whatever slots are free, and only later discover that the interconnect fabric between those...

By Shivlendra Singh Jadoun
September 2, 2026

Google TPU vs NVIDIA GPU in 2026: Which Is Better for Your AI Training and Inference Workloads?

Quick Summary Every team scaling an AI product in 2026 eventually faces the same question, which chip should actually run the models. The Google TPU vs NVIDIA GPU decision used to be simple since...

By Tanuj Chugh
July 27, 2026

GPU Monitoring 101: Tools, Metrics and Best Practices

Quick Summary Quick Summary GPU Monitoring is no longer optional for developers working with machine learning, computer vision, or large language models. It is the difference between catching a memory leak before an overnight...

By Shivlendra Singh Jadoun
July 23, 2026

Inference Cost Optimization: Batching, Caching, and Right-Sizing GPU Instances

Quick Summary Inference Cost Optimization is now the single most important GPU spending decision for any company running AI models in production. This guide covers the complete framework for Inference Cost Optimization in 2026:...

By Deepak Udai
July 22, 2026

How to Deploy Multi-Agent Systems on a Dedicated GPU Server

Quick Summary Multi-Agent Systems are becoming the default architecture for production AI in 2026. This guide walks Indian businesses through deploying Multi-Agent Systems correctly: architecture planning, GPU sizing, orchestration frameworks, security, and cost control....

By Shivlendra Singh Jadoun
July 21, 2026

Types of NVIDIA GPU Architectures in 2026: From Ampere to Blackwell and Vera Rubin

Quick Summary Choosing the right compute layer for AI training and inference starts with understanding NVIDIA GPU Architectures, since every generation, from Ampere through Blackwell and now Vera Rubin, changes the math on memory...

By Shivlendra Singh Jadoun
July 17, 2026

GPU Server for Deep Learning: From Provisioning to First Model Training

Pro Tip Every ML engineer who moves a notebook experiment into a real training run eventually hits the same wall: a laptop GPU cannot keep up with a modern model. This guide walks through...

By Shivlendra Singh Jadoun
July 15, 2026

Ultimate AI Workload GPU Guide: A30 vs A100 vs H100

Quick Summary Choosing the right GPU for your AI workload is one of the most consequential infrastructure decisions an engineering team makes in 2026. The NVIDIA A30, A100, and H100 sit at three very...

By Tanuj Chugh
July 3, 2026

How Much VRAM Do You Actually Need? A Practical Guide for AI, ML, and Rendering Workloads

Pro Tip Understanding how much VRAM you need is the single most critical hardware decision for any AI, ML, or rendering workload in 2026. VRAM requirements vary by model size, batch size, numerical precision,...