Advanced Solutions

Today, the complexity of AI workloads is making technology buyers revisit their tech stack, presenting a great opportunity for partners to solve for the needs of their end customers with a fit-for-purpose and optimized infrastructure.

In a recent survey conducted by IDC, The proliferation of AI throughout nearly every industry led as much as 81% of partners viewing it as an opportunity, rather than a threat, for their practice.¹ 

With this positive note in mind, partners need to be ready when considering AI workload diversity and addressing the needs of the IT buyer. In short, the understanding is that a hybrid model likely works best in most scenarios, with decentralized physical infrastructure networks being the best option moving forward. But let’s dive in and see why this is the case.

The Evolution of AI-Native Infrastructure

First, right sizing an AI-native infrastructure is fundamentally different than a cloud-native infrastructure. The fundamental reason for this is because AI models are not hardcoded or deterministic, so AI-native infrastructures must be built to account for the training and evolution of the model. 

Partners solving the needs of technology buyers must account for that steadily accelerating and shifting workload. In the past, due to external demand, cloud-native infrastructures had to be scalable to meet changing end user demand. AI-Native Infrastructures though, must be scalable to meet the changing demand of the AI-model itself as well as changing end user demand on compute and storage resources.

Some examples of top enterprise workloads that require accelerated infrastructure include AI lifecycle management, engineering applications, as well as text and media analytics. Such intensive workloads are also driving the fastest growing market for AI-native infrastructure. Although not all workloads have equal demand on compute and storage resources. Some examples of less strenuous workloads include AI-enabled networking and security, infrastructure management and supply chain management.²

What an Ideal AI-Native Infrastructure Might Look Like

The ideal framework for an AI workflow to function is one that uses multiple types of chips in an xPU framework, delivering a hybrid-classical AI model workflow. 

In the pre-processing phase, the end user inputs their commands via an AI-PC using computer processing units (CPU) and neural processing units (NPU). NPUs in particular are important here, as they’re made specifically to accelerate AI/machine learning (ML) tasks.

From there, the workflow is distributed to a hybrid compute environment such as a classical high-performance compute solution, made up of CPUs and graphics processing units (GPUs), located either on-premises, through a public cloud or, increasingly, though quantum processing units (QPUs), which are recently being used in this phase of the workflow. 

QPUs present several advantages such as exponential state space memory scaling offered by qubits, as compared to the linear scaling of traditional bits. It's important to note though: if using quantum circuits rather than relying solely on traditional ones, a final post-processing pass must be completed by a classical compute device using CPUs and GPUs, performing a circuit embedding pass and normalizing the data into a classical format. 

Finally, back to the end user, the output data is analyzed by a CPU and NPU on the user’s local AI-ready device.

The near-future state of the market being a highly distributed infrastructure combining quantum and classical computing to connect multiple agentic AI workloads using autonomous operations is extremely exciting, but few technology buyers are likely to have a desire to procure the latest QPU-based infrastructure solutions. The takeaway here is that job completion times are the metric for success when the AI model is value delivery method, so the use of an xPU-based quantum-classical AI workload should at least be considered, especially in the case of AI lifecycle workloads and other demanding tasks.

How TD SYNNEX Can Help You in the Age of AI

In addition to the inherent complexity of AI-native infrastructure, rising IT costs, unstable supply chains and export restrictions, it’s hard for the IT buyer to meet their business needs. That’s why the TD SYNNEX AI Launchpad program exists. 

Our AI Launchpad is a three–to-four-week strategic engagement that helps organizations lay the groundwork for successful AI adoption. Get a concrete action plan, an implementation roadmap and deliver a fit-for-purpose AI solution to your end customer with the TD SYNNEX AI Launchpad.

Want to try it out? Contact ServiceBD@tdsynnex.com to get started.

^Sources

^{1.    IDC. Global Partner Survey — Opportunities and Threats for North America Infrastructure Partners. May 2025.}

^{2.    IDC. Future-Ready Infrastructure Accelerating AI with Fit-for-Purpose Computing. May 2025.}

#ai

#infrastructure
#MachineLearning
#AdvancedSolutions