Why designing on-prem AI systems is harder than ever (and how to get it right)

You’ve made the decision: your company will run AI on-premise.

For many organisations—especially in banking, healthcare, defence, or any regulated industry—this is the only viable path. Data sovereignty, latency, compliance, and control all point in the same direction: keep AI infrastructure inside your environment.

But this is where things get unexpectedly difficult.

Even for experienced IT teams, designing on-prem AI systems today is not an incremental evolution of existing infrastructure practices. It’s a step change. The baseline assumptions have shifted, and the complexity has increased across every layer of the stack.

Let’s unpack why.

The baseline has changed: from CPUs to GPUs

Traditional enterprise infrastructure was built around CPUs, predictable workloads, and relatively stable scaling models.

AI infrastructure is not.

Modern AI systems—especially those involving large language models—are fundamentally GPU-driven. And GPUs introduce a completely different set of constraints:

• Memory bandwidth becomes a primary bottleneck

• Interconnects (NVLink, InfiniBand) matter as much as compute

• Power density and cooling requirements increase dramatically

• Hardware availability and procurement cycles become strategic risks

Choosing “a server” is no longer enough. You’re now designing compute clusters optimised for specific model behaviours.

This is why many teams underestimate the challenge. The infrastructure decisions are no longer generic—they are tightly coupled to the AI workloads you intend to run.

The first hard decision: which GPUs, and how many?

One of the first questions CTOs and CIOs face is deceptively simple:

Which GPUs should we buy?

But the answer depends on multiple variables:

• Model size (7B vs 70B+ parameters)

• Latency requirements (real-time vs batch)

• Concurrency expectations (number of users)

• Precision trade-offs (FP16, INT8, quantised models)

For example, running a 70B parameter model with acceptable latency may require multi-GPU setups with high-speed interconnects. Meanwhile, a smaller model could run efficiently on a single GPU—but might not meet capability requirements.

This is not a procurement decision. It’s an architectural one.

The second challenge: model selection is now an infrastructure decision

In traditional software systems, infrastructure and application layers were loosely coupled.

In AI, they are deeply intertwined.

Choosing a model is not just about capability. It directly impacts:

• Hardware requirements

• Inference latency

• Cost per query

• Scalability of the system

A more capable model may require significantly more GPUs, increasing both capital expenditure and operational complexity.

A smaller model may reduce cost but fail to deliver acceptable outputs, especially in enterprise contexts where accuracy and reliability matter.

This is why enterprise AI is not just about picking the best model—it’s about selecting the right model for your infrastructure constraints and use cases.

The third layer: building a scalable AI stack

Once hardware and models are defined, the next challenge emerges: the AI stack itself.

Running a single use case is manageable. Running multiple use cases across teams is not.

You need to think about:

• Model serving frameworks

• Routing and orchestration layers

• Retrieval systems (RAG pipelines)

• Caching and optimisation strategies

• Multi-tenancy and workload isolation

This is where many teams realise they are not just deploying AI—they are building an internal AI platform.

At Zylon, we often describe this as the transition from AI experimentation to enterprise AI systems.

If you’re interested in how to structure these systems securely and efficiently, we’ve covered related topics around private AI and enterprise deployment in other resources like https://www.zylon.ai/.

The fourth challenge: governance, monitoring, and control

Even if you get the infrastructure and stack right, you’re not done.

Enterprise AI introduces new operational risks:

• Unpredictable model outputs

• Sensitive data exposure

• Lack of observability into usage

• Difficulty enforcing policies across teams

This is where governance becomes critical.

You need:

• Monitoring of model performance and latency

• Usage tracking across teams and applications

• Guardrails to control outputs and access

• Auditability for compliance

This is particularly important in regulated industries, where AI systems must meet strict standards for reliability and traceability.

Without this layer, AI remains experimental—and cannot scale safely.

Why even experienced IT teams struggle

The key issue is not lack of expertise.

It’s that the problem space itself has changed.

Designing on-prem AI systems now requires cross-domain knowledge:

• Infrastructure engineering

• Machine learning systems

• Distributed computing

• Security and governance

Most teams are strong in one or two of these areas—but rarely all of them.

As a result, decisions are often made in isolation:

• Hardware is chosen without fully understanding model requirements

• Models are selected without considering infrastructure constraints

• AI stacks are built without long-term scalability in mind

This leads to costly mistakes, rework, and delays.

A practical way to simplify the process

After working with teams across banking, defence, and healthcare, we’ve seen the same pattern repeatedly:

Teams don’t need more theory.
They need practical tools to make better decisions early.

That’s why we built a free resource:

👉 https://www.zylon.ai/resources/hardware-calculator

The Zylon Hardware Calculator helps you:

• Estimate GPU requirements based on your use case

• Understand trade-offs between models and infrastructure

• Plan capacity for latency and concurrency needs

• Avoid over- or under-provisioning

It’s designed to give you a first, grounded approximation before committing budget or making architectural decisions.

When a second opinion saves months

Even with the right tools, these decisions are high-stakes.

A wrong choice in hardware or architecture can:

• Lock you into suboptimal performance

• Increase costs significantly

• Delay production deployment

That’s why we’re also offering something simple:

👉 https://cal.com/zylon/ai-stack-strategy-session-zylon

A free 30-minute session with one of our AI engineers.

No sales agenda. Just practical guidance.

What you get:

• A second opinion before committing budget

• Clear answers tailored to your stack and constraints

• Insights from real-world deployments across industries

Some teams go on to work with us. Others don’t. Either way, they leave with better clarity.

The bottom line

On-prem AI is not just “harder infrastructure.”

It’s a fundamentally different design problem.

The introduction of GPUs, large models, and enterprise-scale AI workloads has reshaped the requirements. What worked before no longer applies.

But with the right approach—grounded in practical trade-offs, better tooling, and real-world experience—it becomes manageable.

And more importantly, it becomes scalable.

That is the difference between AI that stays experimental and enterprise AI that actually delivers value.


Author: Iván Martínez Toro, Co-Founder & Co-CEO at Zylon
Published: March 27th 2026
Iván leads private, on-premise AI deployments for regulated industries, helping financial institutions, healthcare organizations, and government entities implement secure, sovereign enterprise AI infrastructure.