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GPU Requirements for Deep Learning: How to Size VRAM for Your Workload

September 22, 2026By Nikos TheodotouGPU Hosting

Understanding GPU requirements for deep learning starts with one question: how much VRAM does your workload need? Sizing it wrong wastes budget or crashes training. This guide covers VRAM math, plus CPU, storage, and networking, then matches each tier to real hardware. It helps to check NVIDIA’s GPU specifications for exact figures; HostingB2B’s GPU Servers […]

How to Run Docker Containers with GPU Access

September 17, 2026By Oleg VornicovAI Hosting

GPU-accelerated workloads — model training, inference serving, video transcoding, scientific computing — have become the backbone of modern infrastructure, and Docker remains the standard way to package and ship them. Yet containers are isolated from host hardware by design, so understanding how to use GPU in Docker correctly is a prerequisite for anyone running workloads […]

How to Deploy vLLM for High-Concurrency Inference

August 28, 2026By Nikos TheodotouAI Hosting

vLLM has quickly become the go-to engine for serving large language models at scale — and the difference between a sluggish deployment and one that handles thousands of concurrent requests almost always comes down to how it’s set up. This guide explains how to deploy vLLM for high-concurrency inference, covering hardware planning, installation, and production […]

How to Install CUDA on Ubuntu for Dedicated GPU Infrastructure

August 27, 2026By Oleg VornicovAI Hosting

Setting up a GPU stack correctly is the first hurdle for any AI or machine learning project. Whether you are fine-tuning models on LLM Hosting infrastructure or configuring your own workstation, a broken driver or mismatched CUDA version will stop your workloads before they start. This guide walks through the full process — from a […]