Enterprises are moving beyond AI experimentation towards production deployments, increasing demand for infrastructure that combines accelerated computing, storage, networking and software. Performance remains a priority, alongside GPU utilisation, data sovereignty, energy efficiency and long-term operating costs.
Laurent Chapoulaud, VP EMEA Marketing Professional Solutions, PNY Technologies Europe, discusses PNY’s end-to-end AI Factory approach, the role of NVIDIA RTX PRO Blackwell and opportunities across the Middle East as organisations invest in local AI capabilities.
What will PNY showcase at AI for Everything, and which technologies best reflect your current AI strategy?
At AI for Everything, PNY will showcase its comprehensive end-to-end AI Factory approach, designed to help organisations accelerate their AI ambitions from infrastructure deployment to production-ready AI environments.
Our strategy is built around delivering the complete AI ecosystem required for modern AI workloads. Visitors will discover NVIDIA RTX PRO Blackwell-powered workstations and servers, as well as NVIDIA DGX and HGX platforms that provide the computing power needed for AI training, inference, and advanced data processing.
Beyond compute, we will highlight the critical technologies that enable scalable AI deployments, including high-performance storage and data management solutions, advanced networking infrastructures based on InfiniBand and Ethernet technologies, and enterprise-ready AI software stacks and services.
As a Datacenter Solution Distributor, PNY also brings together a broad ecosystem of technology partners to support every layer of the AI infrastructure. This includes data centre infrastructure solutions from Vertiv, networking technologies from NVIDIA, Broadcom and Celestica, storage platforms from VAST Data and DDN, and software and enterprise services from NVIDIA, Canonical, and F5.
By combining these technologies with our expertise and support services, PNY enables customers and partners to build, deploy, and scale AI factories tailored to their business objectives. Our goal is to simplify the adoption of AI by providing a complete, validated, and high-performance ecosystem that accelerates innovation and time to value.
How are enterprises moving from AI experimentation to production changing demand for GPU-powered infrastructure and high-performance computing?
We are witnessing a significant shift in the AI market. Just a few years ago, most organisations were focused on testing AI through proofs of concept and limited pilot projects. Today, enterprises are moving beyond experimentation and deploying AI solutions at scale across their operations.
This evolution is being driven by the rapid adoption of AI agents, retrieval-augmented generation (RAG) applications, private AI assistants, computer vision solutions, and digital twin platforms used for simulation and optimisation. These workloads require a level of performance, reliability, and scalability that goes far beyond what was needed during the experimentation phase.
As a result, demand for GPU-powered infrastructure and high-performance computing continues to grow. Organisations are investing in high-memory GPUs capable of supporting increasingly complex models, while also strengthening the underlying infrastructure required to operate AI environments efficiently. This includes high-performance networking, scalable storage architectures, and data centre solutions designed to address the growing power and cooling requirements of AI workloads.
What is particularly interesting is that the conversation has changed. The question is no longer, “How do I test AI?” but rather, “How do I industrialise AI while controlling costs and ensuring long-term scalability?” Enterprises are now evaluating their AI infrastructure based on factors such as inference throughput, operational efficiency, scalability, and energy consumption, rather than focusing solely on raw GPU performance.
This is why we see growing interest in integrated AI Factory architectures. Organisations need complete, validated ecosystems that combine compute, networking, storage, software, and services to accelerate deployment while optimising total cost of ownership. The ability to scale AI efficiently and sustainably has become just as important as delivering the highest levels of performance.
What role do NVIDIA RTX PRO Blackwell and data centre solutions play in PNY’s approach to supporting AI training, inference and enterprise workloads?
NVIDIA RTX PRO Blackwell technologies play a central role in PNY’s AI strategy by providing a flexible and scalable platform that addresses a wide range of enterprise workloads. While AI training remains a key priority for many organisations, enterprises are increasingly focused on AI inference, model fine-tuning, visualisation, rendering, simulation, and virtual workstation environments. RTX PRO Blackwell is uniquely positioned to support all of these use cases within a single architecture.
One of the key advantages of the RTX PRO Blackwell platform is its ability to support the entire AI journey. Organisations can begin with AI development and prototyping on RTX-powered workstations, expand to departmental AI and inference deployments, and ultimately scale to larger production environments as their requirements grow. This creates a seamless path from experimentation to operational AI without requiring organisations to completely redesign their infrastructure at each stage.
At the same time, GPU performance alone is no longer enough. Successful AI deployments require a complete data centre ecosystem that combines compute, networking, storage, software, and supporting infrastructure. This is where PNY’s expertise as an end-to-end AI Factory provider becomes essential. By integrating NVIDIA technologies with enterprise-grade storage, high-performance networking, data centre infrastructure, and AI software stacks, we help customers build environments that are optimised for performance, scalability, and operational efficiency.
For many organisations, bringing AI closer to the data is becoming a strategic priority. RTX PRO Blackwell enables enterprises to deploy AI where it delivers the greatest value, whether at the workstation, departmental, or data centre level, while maintaining control over data security, compliance, and costs.
Ultimately, our goal is to help customers accelerate the transition from AI experimentation to production. NVIDIA RTX PRO Blackwell, combined with PNY’s complete portfolio of data centre solutions, allows organisations to deploy AI faster, scale more efficiently, and achieve a sustainable cost structure that supports long-term AI adoption and business growth.
How does PNY help organisations optimise GPU utilisation, scalability and infrastructure costs as AI workloads become more demanding?
As AI projects move into production, organisations face a new challenge: maximising the value of their infrastructure investments while maintaining the flexibility to scale. At PNY, we help customers address this challenge by taking a holistic approach to AI infrastructure design and optimisation.
The first step is ensuring that infrastructure is properly sized for the workload. Not every AI project requires the same level of compute resources, and selecting the right GPU platform is critical to balancing performance, efficiency, and cost. By aligning infrastructure with actual business requirements, organisations can avoid both underutilisation and unnecessary overspending.
We also believe that AI success depends on more than GPU performance alone. Storage, networking, software, and infrastructure components must be designed together from the outset to avoid bottlenecks and ensure optimal resource utilisation. By integrating these layers from day one, we help customers build environments that deliver consistent performance while remaining scalable as workloads evolve.
Another key aspect of our approach is supporting both on-premises and hybrid AI deployments. Many organisations want to take advantage of cloud flexibility while maintaining control over sensitive data, regulatory compliance, and operating costs. By enabling customers to deploy AI resources where they create the greatest value, whether in local data centres, at the edge, or in hybrid environments, we help them maintain data sovereignty while avoiding unpredictable cloud expenses.
Ultimately, optimising AI infrastructure is about achieving the right balance between performance, scalability, and total cost of ownership. Through our end-to-end AI Factory approach, PNY helps organisations build AI environments that can grow with their needs, maximise GPU utilisation, and deliver sustainable long-term value from their AI investments.
What growth opportunities does PNY see across the Middle East as governments and enterprises accelerate investment in AI infrastructure and local AI capabilities?
We see significant growth opportunities across the Middle East as organisations accelerate investments in AI infrastructure and sovereign AI capabilities. As enterprises move from experimentation to production, demand is growing for complete AI ecosystems, including AI factories, accelerated computing, storage, networking, and data centre solutions.
The UAE and Saudi Arabia are establishing themselves as major AI hubs, creating strong momentum across the region. Through our long-standing partnership with NVIDIA, our expanding data centre portfolio, and our regional partner ecosystem, PNY is well positioned to help customers build and scale the AI infrastructure needed to support their long-term innovation goals.
Image Credit: PNY
Source: Tahawul Tech