For years, training has been the central focus for AI infrastructure. The deep learning models, massive clusters, and expensive GPU farms that churn through petabytes of data. But the conversation has started to turn to what happens after the models are trained. What they do once they are deployed into the real world.
This is known as AI inference: the process of running live, unseen data through a trained AI model to generate predictions, make decisions or produce content. The outputs can be seen in apps that people use everyday.
Why the focus on inference all of a sudden?
Model training is a huge investment, but it’s a periodic cost. Inference is continuous, it’s real-time. It means that organisations need infrastructure that’s not only fast, but predictable, resilient and stable.
There are a few reasons this has come to the fore:
Sustained workload: enterprises are moving beyond prototypes
Once models go into production, they’re answering requests 24 / 7. Businesses are starting to realise that if infrastructure is not built for a constant, sustained load, that’s where it can buckle under real-world pressure. This is precisely why so many AI prototypes fail to reach production.
Nothing exemplifies this better than agentic AI: systems that can reason, plan, take actions, and coordinate with other systems are fast becoming the next evolution beyond copilots and chat interfaces.
But agents don’t just generate responses. They execute sequences of decisions. They call tools, query databases, trigger workflows, interact with APIs, and loop through reasoning steps multiple times per task. Each of those steps is an inference call. Multiply that across thousands or millions of users, and inference volume increases dramatically.
Agentic systems raise the bar for latency, reliability, orchestration, and fault tolerance – exposing infrastructure weaknesses quickly as businesses race to be first-to-market. And in reality, most organisations are still prototyping agents in isolation; hard-coding workflows that don’t adapt; treating agents as tools, not systems; and struggling with observability, safety and cost control. The pressures of AI inference are coming to the fore for enterprises more vividly than ever before.
One size doesn’t fit all: cloud, on-prem, edge and Lenovo
While training clusters and model size have commanded keynote headlines, the day-to-day operational demands of inference have rarely taken centre stage. Lenovo’s keynote at CES in January changed that.
The company outlined its purpose-built AI inferencing servers and a hybrid AI strategy designed to support production workloads across cloud, on-premises environments, and the edge.
This is significant.
Why? Because a lot of AI infrastructure still lives in the public cloud, and for good reason. Clouds are elastic; they can scale up and down with demand, which is great for unpredictable workloads. But they’re not always ideal for inference:
- Latency matters: even seconds can impact user experience in today’s real-time economy.
- Data movement costs money: especially if you’re sending lots of sensitive data back and forth.
- Compliance and privacy: sometimes these demands require data to stay near where it’s generated.
Agentic systems, in particular, amplify this reality. When AI systems are reasoning, taking actions, and coordinating across tools in real time, they demand low latency, predictable performance, and sustained throughput. Pushing all of that through a single cloud environment is not always optimal.
Lenovo’s emphasis on hybrid deployment and flexible consumption models acknowledged that running AI at scale isn’t solely about performance, it’s about placing inference where it makes the most operational and economic sense.
This is why hybrid strategies, spanning public cloud, private on‑premises systems, and edge compute, are increasingly important. For some workloads, running inference as close as possible to the device or data source makes a huge difference in responsiveness and cost.
In other cases, keeping inferencing infrastructure on‑prem gives teams more control over performance and security without relying on third‑party networks.
In terms of edge computing, it’s about bringing intelligence to where the action is. Robotics is a great example here, as outlined in our previous article, calling for split-second decisions.
As this trend grows, the infrastructure that supports local inference has to be rugged, power‑efficient, and capable of predictable performance even when connectivity fluctuates. That’s a very different set of trade‑offs compared with traditional cloud deployments.
Why resilience and reliability matter more than ever
Running inference in production is about engineering systems that keep working even when parts fail, traffic spikes, or network connections are spotty, as well as ensuring that those systems are future-proofed, governable and highly adaptable in what is an incredibly fast-moving market.
This means:
- Modularity – systems designed to plug in new models or vendors without rewrites.
- Observability & cost control – real-time visibility into usage, spend, and performance so you can respond quickly to issues and ensure you don’t blow your budget overnight just by responding to more requests.
- Compliance-first design – embedding explainability, auditability, and jurisdictional alignment.
- Ethical evolution – ensuring systems scale responsibly, not just quickly.
As teams think about deploying AI at scale, the focus needs to move beyond just the models, and look at how to run these models reliably, and where they make the most sense to run, whether that’s in the cloud, on‑premises, or at the edge. Successfully operationalising AI means thinking about inferencing infrastructure as an architectural foundation that needs thoughtful design, not an afterthought.
