Data CentersData Centers contains the computing infrastructure that IT systems require, such as servers, data storage drives, and network equipment.Check out our NeuroNet Data Center Partners and Get Your NeuroNet Virtual Private Server Up and Running!NeuroNet Provides Multiple Cloud Based Data Centers. Check out the following links to learn how it works!
Taiko NodeTaiko is a fully permissionless, Ethereum-equivalent based rollup. Inspired, secured, and sequenced by Ethereum. Taiko is the most developer-friendly and secure Ethereum scaling solution, by Taiko Labs https://taiko.xyz
Deploy a Taiko Node using NeuroNetTo get started with NeuroNet, you'll need to connect your preferred non-custodial EMV compatible Wallet. This procedure is simple and straightforward.▪️ Connecting Your Wallet to the NeuroNet DashboardOnce You've Successfully Connected Your Wallet, If Needed, Add Credit.▪️ Adding Credit to Your NeuroNet DashboardSelect "Deploy" from the Virtual Machine menu.Configuring Your VPS Adding a VPS NameWhen setting up your Virtual Private Server VPS, it's important to give it a unique, recognizable name. This allows for easier identification and management, especially when you're dealing with multiple servers. So take a moment to think of a suitable name for your VPS. In this case, we will go with "taiko-node"
Recommended VPS Configuration for Running a Taiko NodeTo configure your Virtual Private Server (VPS) for optimal performance, you need to select the appropriate computing specifications. We recommend the following configuration for running a Taiko node: 16 GiB of memory, 4 vCPUs for processing power, and 2TB of storage space. However, if 2TB is not feasible for you, make sure you have a minimum of 1TB storage to ensure smooth operation of the Taiko node.
Deploying Taiko Node, follow the Taiko Official Guide here:https://docs.taiko.xyz/guides/node-operators/run-a-taiko-node-with-docker/
What is GPU?
GPU, short for Graphics Processing Unit, is a specialized computing unit designed for tasks related to graphics and video processing. Unlike CPUs (Central Processing Units), GPUs are designed specifically for parallel processing of large amounts of data.
High Parallel PerformanceGPUs are composed of hundreds or thousands of small cores, allowing them to process a large amount of data simultaneously. For example, when rendering 3D graphics, each core can independently process a pixel or a vertex, significantly increasing processing speed.
Graphics OptimizationOriginally designed to accelerate graphics rendering, GPUs are efficient at handling tasks related to images and videos, such as texture mapping and lighting calculations.
Wide Range of ApplicationsWhile GPUs were initially designed for gaming and professional graphics design, they are now also crucial in many other fields, especially in artificial intelligence and machine learning.Gaming and Artificial Intelligence
Why Do We Need GPUs?
The high parallel processing capability of GPUs makes them excel in handling graphics-intensive tasks and large-scale data processing tasks, making them indispensable in gaming and artificial intelligence fields.
Currently, the market value of the GPU chip leader NVIDIA exceeds $1 trillion, which is six times that of the CPU chip leader Intel, indicating a huge demand for GPUs, far exceeding that of CPUs.
GamingGames and modern gaming typically involve complex 3D graphics and physics simulations. These tasks require extensive parallel processing, making the powerful graphics processing capabilities of GPUs highly suitable. Using GPUs can achieve smoother gaming experiences and higher graphical fidelity.
Artificial Intelligence and Machine LearningIn the field of artificial intelligence, especially in deep learning, handling large amounts of data and performing complex mathematical computations are required. These computing tasks are often parallelizable, making them highly suitable for the high parallel performance of GPUs. Using GPUs can significantly accelerate the speed of model training and inference.