<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=1232938&amp;fmt=gif">
redapt - rack integration - white icon
Data Center Infrastructure

Enhance your data center infrastructure with tailored solutions that boost performance and efficiency, ensuring rapid growth and exceptional customer experiences.

redapt - data estate assessment - white icon
Cybersecurity

Fortify your operations with comprehensive cybersecurity solutions that deliver resilient protection and end-to-end risk mitigation.

redapt - cloud adoption ready - white icon
Managed Cloud Services

Align your cloud strategy with your business objectives through our end-to-end managed services, delivering expert oversight across infrastructure, data optimization, and cost control.

Cloud_Adoption
Cloud Adoption

Adopt the cloud confidently with expert guidance on capacity, cloud-native technologies, and a step-by-step path for successful migration.

redapt - devops adoption - white icon
DevOps

Energize your software development lifecycle with tailored DevOps to match your needs and workflows.

redapt - data science experiment - white icon
Data Analytics

Successfully adopt advanced analytics capabilities to unlock insights, inform the design of your products, and make smarter decisions.

Artifical_Intelligence
Artificial Intelligence

Leverage Artificial Intelligence to generate actionable insights, uncover new revenue opportunities, and drive more informed decision-making.

Application_Modernization
Application Modernization

Modernize your applications with advanced development methodologies, driving greater agility, efficiency, and continuous innovation to excel in today’s competitive environment.

BLOG
The latest in infrastructure, technology, and security

From emerging innovations to real-world applications, we cover what helps leaders navigate complexity, drive transformation, and make smarter decisions in a rapidly evolving landscape.

VIDEO CENTER
Go deeper with expert stories, insights, and strategy

Your destination for expert conversations, client stories, and diving deep into the latest in infrastructure, technology, and business strategy.

CUSTOMER STORIES
Discover how we elevate organizations

Read some of our customer stories to learn more about how we develop and implement solutions, along with how those solutions have helped our clients and partners.

KNOWLEDGE CENTER
Stay informed with expert guides, trends, and webinars

Explore our curated collection of eBooks, guides, and webinars designed to help leaders stay informed and ahead of trends.

redapt-employee-unboxing-tech
ABOUT US
Get to know our mission, team, and what drives us

We specialize in implementing and managing technical solutions to support your infrastructure and digital environments. 

RC_DC_6481
LEADERSHIP
Meet the leaders driving innovation and customer success

Bringing together decades of experience in technology, business strategy, and customer success.

What the company needed Image-1
CAREERS
Join a team built on impact, collaboration, and growth

Build lasting relationships and deliver real-world results.

Actionable Insights.

Make-or-Break Focus Areas.

Experts Save You Time.

Let our experts save you time, money, and stress as you explore solutions. Talk to an expert today!

Contact Us

  • There are no suggestions because the search field is empty.
Banner Bg Image

High-Performance Networking to Support Critical Workloads for AI and ML

Among the list of challenges organizations often encounter when adopting advanced tools like artificial intelligence (AI) and machine learning (ML), one that can easily be overlooked is the need for high-performance networking. Specifically, networking that provides both high bandwidth and low latency.

Without high bandwidth, intensive AI and ML workloads don’t have the horsepower needed to run efficiently. Consider how annoying it is when a YouTube video stops to buffer, then amplify that annoyance by 1000x.

Similarly, not having low latency would undermine the effectiveness of AI tools such as chatbots, leading to frustration for users. And attempting to use even more advanced tools, such as predictive analytics models, would deliver results at such a slow pace they would essentially be too outdated to be used at all.

So how do organizations ensure they have enough bandwidth in place while also reducing latency to as close to zero as possible? In most modern datacenters, the key is spine leaf architecture.

An overview of spine leaf architecture

Traditional datacenters were constructed with a three-tier architecture consisting of core routers, distribution (or aggregation) switches, and access switches.

In order to reduce the potential for a condition known as a broadcast storm, which severely disrupts the normal operation of the network, a protocol named Spanning Tree was typically implemented. Spanning Tree helps prevent network loops which cause broadcast storms, but it has several issues, including slow convergence times which can result in the unnecessary disruption of network traffic.

tree-chart_1

While this three-tier structure was effective, it had some rather severe drawbacks, including high latency and energy needs. Put another way, it could be slow and a power hog.

Spine leaf architecture, in comparison, reshuffles the network topology to greatly reduce latency by cutting down the number of hops between endpoints within a datacenter. The leaf nodes are where all of your compute and storage resources are connected (access layer), and each of these leaf nodes are connected to the spines in the datacenter in order to make the resources connected to the leaf nodes one hop away from each other.

To help wrap your brain around this, here’s a basic topology diagram:

tree-chart_2

Beyond the minimal latency, spine leaf architecture is highly scalable and fault tolerant. If you start out with, say, 16 leaf nodes connected to 2 spines, you can easily expand that to 32 leaf nodes and 4 spines as your needs change.

This scalability also means you can increase the amount of your bandwidth without the need to grow the number of racks in your datacenter. Multiple connections can be made between the spine nodes and leaf switches. This allows for equal cost multi-pathing between various nodes—basically, you’re able to leverage the bandwidth across all of your links in a much more efficient way, which dramatically increases throughput.

For example, if you outgrow your bandwidth capacity provided by two leaf-to-spine links, it’s relatively simple to add two more links to grow and scale the amount of bandwidth you have available.

And when properly implemented, the high degree of fault tolerance provided by a spine leaf architecture means the impact from the loss of a link or device is minimal and rarely impacts the user experience. This high level of fault tolerance also facilitates the ability to perform network maintenance activities without service outages.

Getting the right networking in place

Ultimately, whatever shape your networking takes will depend on some key factors. 

One is your organization’s overall readiness to adopt advanced analytics tools like AI and ML. Another is what your eventual AI and ML workloads will need—how much bandwidth, how frequently your need results from your models, and more.

You can learn more about how you can get the right infrastructure in place for advanced analytics tools in our comprehensive AI and ML resource. And if you’re ready to build out your organization’s advanced analytics capabilities—not just when it comes to networking, but for every facet of the process—contact one of our experts.