In-houseArtificial Intelligence (implemented in the company’s own facilities, with its own hardware) is becoming a strategic bet for medium and large organizations in Spain and companies around the world. Unlike cloud solutions, bringing AI “home” allows for greater data control, cost reduction at scale and adaptation to specific measures. Many companies have already taken the step, optimizing internal processes ranging from data analysis to cybersecurity. Below, we explore why companies are adopting in-house AI, successful examples across a range of industries, and current trends in servers and workstations designed for on-premises AI.
Advantages of in-house AI over the cloud
Implementing AI with your own infrastructure has a series of clear benefits compared to delegating these workloads to the cloud:
Data control and compliance: Sensitive information never leaves the company, which prevents exposing confidential data to third parties. In the cloud, data must be decrypted to be processed by models, with the risk of leaking proprietary information. Keeping AI in-house improves privacy and makes it easier to comply with regulations (e.g. GDPR in Europe) by having sovereignty over the data.
Cost savings in the medium-long term: Although the initial deployment requires investing in hardware, as AI projects grow it is often cheaper to operate with in-house resources. A recent study showed that the cost per inference of self-hosted models is consistently lower than using equivalent cloud services, with savings of hundreds of thousands of euros per year for large deployments.
Avoid dependencies and legal risks: Having an internal AI platform reduces dependency on external providers (vendor lock-in) and ensures business continuity. In addition, it mitigates emerging legal risks associated with third-party models, such as possible litigation for the use of models trained with data of dubious origin.
Customization and performance: A custom infrastructure can be optimized for the company’s specific use cases, integrating seamlessly with internal systems. The latency of sending data to the cloud is eliminated, which is vital for real-time applications.
Planned scalability: With its own hardware, the company decides how and when to scale its AI capabilities, adding new GPUs, servers or storage as demand grows, with no surprises on the monthly bill.
In short, in-house AI offers control, savings and flexibility, which is why many leading companies are already investing in it. Let’s look at some real-life cases of organizations that have incorporated AI with local hardware to improve their internal processes.
Success stories: In-house Artificial Intelligence in action
Several Spanish and European companies from sectors such as retail, finance, industry and technology are already reaping the benefits of in-house AI, having optimized key processes with artificial intelligence solutions deployed in their own data centers. These examples demonstrate tangible results, serving as inspiration for those looking to keep their data in-house without giving up innovation:
Retail – Inditex optimizes demand and logistics: Inditex has embraced AI to gain efficiency throughout its value chain. It developed its own algorithms that predict demand for new items and adjust stocks in real time in each store. It also implemented AI in its logistics centers to assign task rotations to staff and classify garments on conveyor belts, optimizing time and reducing surpluses.
Banking and insurance – Generative AI for customer service and decisions: BBVA has developed its own conversational virtual assistant to handle customer queries and generative AI tools that help its employees be more efficient. In the insurance sector, a European company automated 89% of its queries with AI, generating a return of millions per year.
Industry – Quality control and predictive maintenance: BMW integrated machine vision systems in its factories to inspect parts for defects. Several companies have reduced maintenance costs by 30% by applying AI to predict failures before they occur.
Cybersecurity – Real-time monitoring: Telefónica Tech has deployed internal AI models that analyze security data and detect vulnerabilities before they are exploited.
In-house AI hardware trends
Today’s market offers a multitude of options in AI-optimized servers, GPUs, CPUs, and storage, enabling everything from small AI labs to full-fledged high-performance data centers:
Next-generation GPUs: NVIDIA dominates the industry with GPUs like the A100, H100, and the new H200, designed for deep learning. AMD offers alternatives such as MI250/MI300 Instinct.
Specialized AI Servers: Solutions such as the Asus ESC8000A-E12, Gigabyte G292-Z20, Asrockrack 1U4G-ROME, and Supermicro SYS-420GP-TNR offer advanced, custom configurations with multiple GPUs to train complex models locally.
Ultra-Fast Storage: AI-optimized All-Flash NVMe solutions ensure maximum data processing speed, reducing latencies and improving operational efficiency.
AI Workstations for Business: Professional PCs with RTX 6000 Ada GPUs, AMD Threadripper, and NVMe SSD storage deliver AI capability on corporate desktops.
Conclusion: Preparing Your Business for AI with Advanced Hardware measure
The examples and trends described make it clear that in-house AIis no longer a futuristic aspiration, but a real competitive advantage. Leading companies have achieved millions in savings, efficiency improvements and innovative new services by integrating AI solutions into their internal systems.
At Ibertrónica, with decades of experience in hardware solutions, we can help you design an AI infrastructure tailored to your business. Whether it is a multi-GPU server, an AI HPC cluster or optimized workstations, we advise you so that you get the maximum return per euro invested. The technology is ready; the time to bring Artificial Intelligence home is now. Contact us at ibertronica.es and discover the AI hardware solutions that will transform your company.
