Inference is becoming Europe’s core AI workload. Telcos are moving fast on low-latency infrastructure. Data sovereignty is shaping every deployment decision.
At GTC Europe, these trends were impossible to miss. The conversation has moved beyond experimentation to execution, with exciting, distinctly European priorities shaping conversations.
Gcore’s own Seva Vayner, Product Director of Edge Cloud and AI, shared his take on this year’s event during GTC. He sees a clear shift in what European enterprises are asking for and what the ecosystem is ready to deliver.
Scroll on to watch the interview and see where AI in Europe is heading.
“It’s really a pleasure to see GTC in Europe”
After years of global AI strategy being shaped primarily by the US and China, Europe is carving its own path. Seva notes that this year’s GTC Europe wasn’t just a regional spin-off. it marked the emergence of a distinctly European voice in AI development.
“First of all, it's really a pleasure to see that GTC in Europe happened, and that a lot of European companies came together to have the conversation and build the ecosystem.”
As Seva notes, the real excitement came from watching European players collaborate. The focus was less on following global trends and more on co-creating the region’s own AI trajectory.
“Inference workloads will grow significantly in Europe”
Inference was a throughline across nearly every session. As Seva points out, Europe is still at the early stages of adopting inference at scale, but the shift is happening fast.
“Europe is only just starting its journey into inference, but we already see the trend. Over the next 5 to 10 years, inference workloads will grow significantly. That’s why GTC Europe is becoming a permanent, yearly event.”
This growth won’t just be driven by startups. Enterprises, governments, and infrastructure providers are all waking up to the importance of real-time, regional inference capabilities.
“There’s real traction. Companies are more and more interested in how to deliver low-latency inference. In a few years, this will be one of the most crucial workloads for any GPU cloud in Europe.”
“Telcos are getting serious about AI”
One of the clearest signs of maturity at GTC Europe was that telcos and CSPs are actively looking to deploy AI. And they’re asking the hard questions about how to integrate it into their infrastructure at a vast scale.
“One of the most interesting things is how telcos are thinking about adopting AI workloads on their infrastructure to deliver low latency. Sovereignty is crucial, especially for customers looking to serve training or inference workloads inside their region. And also user experience: how can I get GPU capacity in clusters, or deliver inference in just a few clicks?”
This theme—fast, sovereign, self-service AI—popped up again and again. Telcos and service providers want frictionless deployment and local control.
“Companies are struggling most with data”
While model deployment and infrastructure strategy took center stage, Seva reminds us that data processing and storage remains the bottleneck. Enterprises know they need to adopt AI, but they’re still navigating where and how to store and process the data that fuels it.
“One of the biggest struggles for end customers is the data: where it’s processed, where it’s stored, and what kind of capabilities are available. From a European perspective, we already see more and more companies looking for sovereign data privacy and simple, mature solutions for end users.”
That’s a familiar challenge for enterprises operating under GDPR, NIS2, and other compliance frameworks. The new wave of AI infrastructure has to be built for performance and for trust.
AI in Europe: responsible, scalable, and local
Seva’s key takeaway is that AI in Europe is no longer about catching up, it’s about doing it differently. The questions have changed from “Should we do AI?” to “How do we scale it responsibly, reliably, and locally?”
From sovereign deployment to edge-first infrastructure, GTC Europe 2025 showed that inference is the foundation of how European businesses plan to run AI. “The ecosystem is coming together,” explains Seva. “And the next five years will be crucial for defining how AI will work: not just in the cloud, but everywhere.”
If you’re looking to reduce latency, cut costs, and stay compliant while deploying AI in production, we invite you to download our free ebook, The inference optimization playbook.
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3 clicks, 10 seconds: what real serverless AI inference should look like
Deploying a trained AI model could be the easiest part of the AI lifecycle. After the heavy lifting of data collection, training, and optimization, pushing a model into production is where “the rubber hits the road”, meaning the business expects to see the benefits of invested time and resources. In reality, many AI projects fail in production because of poor performance stemming from suboptimal infrastructure conditions.There are, broadly speaking, two paths developers can take when deploying inference: DIY, which is time and resource-consuming and requires domain expertise from several teams within the business, or the ever-so-popular “serverless inference” solution. The latter is supposed to simplify the task at hand and deliver productivity, cutting down effort to seconds, not hours. Yet most platforms offering “serverless” AI inference still feel anything but effortless. They require containers, configs, and custom scripts. They bury users in infrastructure decisions. 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And if the user experience is unclear or inconsistent, customers end up frustrated—or worse, they churn.Developers and data scientists don’t want to manage infrastructure. They want to bring a model and get results without becoming cloud operators in the process.Dom Wilde, SVP Marketing, MirantisThat’s why deployment UX is no longer a nice-to-have. It’s the core of your product.The benchmark: 3 clicks, 10 secondsWe built Gcore Everywhere Inference to remove every unnecessary step between uploading a model and running it in production. That includes GPU provisioning, routing, scaling, isolation, and endpoint generation, all handled behind the scenes.The result is what we believe should be the default:Upload a modelConfirm deployment parametersClick deployAnd within ten seconds, you’re serving live inference.For platform teams supporting AI workloads, this isn’t just a better workflow. 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The platform handles the complexity so our customers can focus on building value.Dom Wilde, MirantisIf it’s not 3 clicks and 10 seconds, it’s not really serverlessThere’s a growing gap between what serverless inference promises and what most platforms deliver. Many cloud providers are focused on raw compute or orchestration, but overlook the deployment layer. That’s a mistake. Because when it comes to customer experience, ease of deployment is the product.Mirantis saw that early on and partnered with Gcore to bring inference-as-a-service to CSP and enterprise customers, fast. Now, customers can launch new offerings more quickly, reduce operational overhead, and improve the user experience with a simple, elegant deployment path.Redefine serverless AI with GcoreIf it takes a config file, a container, and a support ticket to deploy a model, it’s not serverless—it’s server-less-ish. With Gcore Everywhere Inference, we’ve set a new benchmark: three clicks and ten seconds to deploy AI. And, our model catalog offers a variety of popular models so you can get started right away.Whether you’re frustrated with slow, inefficient model deployments or looking for the most effective way to start using AI for your company, you need Gcore Everywhere Inference. Give our experts a call to discover how we can simplify your AI so you can focus on scaling and business logic.Let’s talk about your AI project
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