Hammer launches on-premises AI platform for sovereign use

SOFIAH NICHOLE SALIVIO

News Editor

Hammer has launched Hammer Stack, an on-premises AI infrastructure platform for businesses, positioning it as part of a wider shift towards sovereign AI infrastructure.

The launch comes as many companies struggle to move artificial intelligence projects from pilot stages into day-to-day use. Hammer argues that the main obstacle is not demand for AI tools, but the difficulty of building and running the underlying infrastructure at scale.

Many organisations began AI work in public cloud environments because they offered an easy starting point for experimentation. But as workloads expand, those environments can become harder to manage, with costs rising and large datasets becoming expensive to move.

This issue, often described as data gravity, can leave businesses with data stored in one environment and computing resources tied to another. Hammer says that separation creates architectural constraints that hinder performance and make it harder to generate a return on AI spending.

Sovereign focus

Hammer Stack is designed as a fully integrated platform that keeps AI workloads on premises rather than in hyperscale public cloud systems. It is intended to let organisations decide where models are trained and deployed, with an emphasis on data control, policy requirements and regulatory obligations.

The platform combines AMD EPYC processors, NVIDIA GPUs and networking, and VDURA storage in a rack-level design. The package also includes APC power management and support for liquid cooling.

Rather than selling it as a standalone system, Hammer has presented Hammer Stack as the hardware layer for its existing Hammer AI Works ecosystem. That broader offering includes advisory services, a community model and a sandbox environment called The Labouratory for testing proof-of-concept projects before production deployment.

Production barrier

Hammer argues that many AI initiatives fail not at the software stage, but in the move from test environments into production systems. In its view, piecemeal infrastructure decisions can create what it calls “accidental architectures” that work in isolated trials but break down under full operational demands.

The new platform’s rack-level validation is intended to reduce that risk by integrating compute, storage, networking, power and cooling into a pre-designed module. The aim is to give partners and customers a more predictable route from experimentation to live deployment.

Hammer is also linking the product to a wider market debate over sovereignty in AI systems. For many European organisations, especially those handling sensitive data, questions over where information sits, who controls access to it and how it is processed have become more prominent as AI use expands.

Those concerns have helped create demand for infrastructure that can run advanced AI workloads without relying entirely on large US cloud providers. Hammer is seeking to position itself in that market by offering an option that keeps data and compute resources under customer control on site.

Channel role

Hammer operates as a value-added distributor focused on enterprise infrastructure, cybersecurity and data products across Europe. Hammer Stack will be supported by specialist AI consultancies alongside its channel network, reflecting the technical complexity of designing and managing AI systems in production.

Financing will also form part of the offer, with leasing and subscription-style models available to customers that want to align infrastructure spending with actual use. That approach is aimed at reducing the upfront cost of deploying AI systems on premises at a time when many businesses remain cautious about large capital commitments.

The launch reflects a broader effort by suppliers across the IT market to address frustration over AI returns. While spending on generative AI and large language model projects has surged, many businesses are still trying to prove commercial value from those investments once trial projects end.

Hammer framed that gap between ambition and implementation as one of the defining issues in the current AI market. It argues that organisations need infrastructure that matches their own data location, governance rules and operational priorities, rather than defaulting to the architecture of a hyperscale cloud provider.

“AI should run where your data, policies, and priorities dictate, not where a hyperscaler decides,” Hammer said.