In this news

If 2022 is the year generative AI (GenAI) went mainstream, 2023 is the year it went viral. Within the last 12 months, large language models (LLMs) like ChatGPT-3.5 and Google Bard, and latent diffusion models (LDMs) like Stable Diffusion, Midjourney and DALL-E were released and became household names.

Navigating the fine line between consumer interest and mounting market pressure, business and IT leaders across industries have since been asking non-stop how to implement these exciting advances in AI as quickly as possible.

Our advice is to take a proven and practical approach to AI, one we've been practicing for nearly a decade through our work with clients and applied AI/ML research, development, and fieldwork. As critical as it is to have the right approach to AI solutions, it is equally as essential to have the right architecture in place to support your AI goals.

But where do you begin?

WWT's composable AI lab

From keeping up with new software and hardware releases; to building, training, testing and integrating AI models; to modernizing data strategies, infrastructure and operating models; to figuring out how to pay for it all in a sustainable way; to pinpointing the right use cases to invest in — the complexity of the AI marketplace can feel overwhelming.

That's why we're excited to announce that WWT clients will soon be able to compare and validate AI models and solutions at scale in a new composable AI lab environment housed within our Advanced Technology Center (ATC).

In this dedicated AI lab environment, clients can evaluate different AI/ML offerings at scale using an array of high-performance architectures that represent the best hardware and software solutions in the industry.

The ATC advantage

High-performance architecture brings high-performance computing (HPC) — which we introduced to the ATC in 2019 — and AI/ML environments together into one architectural framework that comprises the core IT infrastructure components needed to meet the intense data demands of advanced solutions like GenAI.

The way your compute, storage, memory, automations, monitoring tools, security configurations and more work together matters tremendously. The way the architecture is optimized impacts how you can train your AI model, how you will be able to learn from or extract data from the model, and how you will operate the model — all with the least amount of overhead or expense.

High-performance architecture (HPA) incorporates all these different components of technology into a system that is built to efficiently drive performance and reliability.

WWT's AI lab environment will enable the testing, training and deployment of LLMs and AI-powered solutions.


Our AI lab has been designed to reduce risk and accelerate your speed-to-decision at nearly every step of the journey to AI solution deployment.

The lab can be used to compare, test, validate and train AI models so the solution is ready for your customers or employees to use once deployed into your environment. The AI lab will enable WWT clients to explore these high-value use cases and many more based on their unique business objectives and goals.

Because our new AI lab environment is composable, different vendor components can be swapped in and out, allowing for fast customization and scaling, and enabling you to test different HPA configurations depending on your current environment, future needs and business objectives.

AI lab: Outcomes snapshot  

Organizations are already using our new AI lab environment to tackle AI/ML use cases from beginning to end. Here's a snapshot of the different challenges we are helping clients overcome:

AI ecosystem enablement

  • Thermal modeling and ESG impact estimation
  • GPU capacity forecasting and right-sizing
  • AI-stack comparisons (e.g., InfiniBand vs. Ultra-Ethernet)
  • Public cloud vs. specialist GPU cloud vs. on-prem tech comparisons
  • Total cost of ownership (TCO) estimation for SaaS vs. custom AI products

Generative AI and deep learning

  • LLM fine-tuning (on-premises and cloud options)
  • Computer vision and image modeling
  • Vector DBs selection and LLMOps

Edge-compute and AI inference

  • Edge frameworks and AI inference (e.g., on-device vs. on-cloud)
  • Testing LLM/GenAI embeddings in edge-compute products

Foundational data capabilities

  • Digital twins, AI workload replication
  • Federated machine learning
  • AI middleware: data catalogs, lineage tools, etc.

Ongoing investment in client success

Our investment to create this new AI Lab is an evolution of the commitment we made when we first created the ATC as a place for our clients and partners to make smart technology decisions faster and with less risk. The complexities and capabilities of the labs we develop continue to increase as technology advances.

We made a similar investment to support our clients' outcomes when we created our Flash Lab, a multi-vendor environment that provides a fully integrated network, storage and compute platforms for proofs of concept and testing. Our Flash lab had a tremendous impact on accelerating time-to-value for our clients, and we expect a similar result from our AI lab.

The new AI Lab will give our clients the certainty they need to make the right investments in AI solutions that can transform their organizations. We're excited about this opportunity to continue to innovate with our clients as well as the opportunity to showcase our technology partners' latest innovations in AI solutions and high-performance architecture.

What we're doing today

We are currently working with many clients on building and optimizing AI architectures, LLMs and AI-powered solutions with great insights and outcomes. By continuing to invest in our ATC lab environments, we're looking forward to providing our AI services at scale, with the goal of making it easier for clients and partners to learn about, test and deploy AI/ML solutions that deliver real business results.

We hope you'll join us on this exciting journey as we continue to scale the capabilities of our new AI lab environment.