At the beginning of May 2023, Gcore thwarted a 500 Million PPS DDoS attack with a total capacity of more than 800 Gbps. It was the largest DDoS attack company’s customers suffered since the beginning of 2023.
The attackers used multiple approaches, as Andrey Slastenov, Head of Web Security at Gcore, explains:
“Firstly, attackers targeted the game app at the L7 level with a large amount of traffic, which suggests that they were trying to overload the application layer of the gaming company’s infrastructure. They probably used botnets or amplification techniques. After this initial attack, the attackers switched tactics and continued the DDoS assault using a huge volume of small packets.”
Most attacks have relatively low speeds—one million PPS or less—but for web resources without protection that might be enough to be put out of action. An hour of downtime due to DDoS attacks in the gaming industry often costs upwards of $25,000.
How Gcore Stopped the Attack
Gcore used a distributed infrastructure and peering partners to spread traffic across multiple locations, reducing the impact of the attempted DDoS attack. Gcore’s protection team detected and mitigated the attack quickly, preventing disruption to the client’s web application. This highlights the importance of having a robust DDoS protection strategy and working with trusted security partners to ensure the availability and resilience of online services.
Why Are Cybercriminals Attacking Gaming Companies?
DDoS attacks are unfortunately common in the gaming industry, where attackers may target game servers, gaming websites, or even individual players. There are several reasons why DDoS attacks may target gaming companies:
- Desire for financial gain; attempting to extort money from the gaming company to stop the attack
- Attempt to disrupt gaming operations, gain a competitive advantage, or damage a company’s reputation, either by cybercriminals or competitors
- Retaliation by a player who feels unfairly treated by a gaming company
Regardless of the motivation behind the attack, DDoS attacks can cause severe damage to the gaming industry, leading to significant disruptions in online gaming services, and resulting in frustrated players, lost revenue, and reputational harm. The impact of such attacks can be devastating, as they can paralyze game servers, overwhelm website traffic, and compromise the integrity of player data.
How to Protect Against Powerful DDoS Attacks
To ensure the continuity of gaming operations, it is crucial for companies to prioritize DDoS protection and work closely with trusted security partners to identify and mitigate potential threats.
To mitigate DDoS attacks, companies must use DDoS protection services, deploy firewalls and intrusion prevention systems, and regularly test their security measures. Additionally, a gaming company can work with its internet service provider (ISP) and web protection provider to identify and block malicious traffic and improve DDoS resilience.
Protect yourself with Gcore—we provide protection at L3, L4, and L7 levels. Our solution has over 1 Tbps total filtering capacity and blocks DDoS attack from the first request by sessions, not IPs. Keeps your gaming services safe with us.
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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 2 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 opt for 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. And they often assume your data scientists are also DevOps engineers. It’s a far cry from what “serverless” was meant to be.At Gcore, we believe real serverless inference means this: three clicks and ten seconds to deploy a model. That’s not a tagline—it’s the experience we built. And it’s what infrastructure leaders like Mirantis are now enabling for enterprises through partnerships with Gcore.Why deployment UX matters more than you thinkServerless inference isn’t just a backend architecture choice. It’s a business enabler, a go-to-market accelerator, an ROI optimizer, a technology democratizer—or, if poorly executed, a blocker.The reality is that inference workloads are a key point of interface between your AI product or service and the customer. If deployment is clunky, you’re struggling to keep up with demand. If provisioning takes too long, latency spikes, performance is inconsistent, and ultimately your service doesn’t scale. 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. It’s a transformation.With Gcore, our customers can deliver not just self-service infrastructure but also inference as a product. End users can deploy models in seconds, and customers don’t have to micromanage the backend to support that.Dom Wilde, MirantisSimple frontend, powerful backendIt’s worth saying: simplifying the frontend doesn’t mean weakening the backend. Gcore’s platform is built for scale and performance, offering the following:Multi-tenant GPU isolationSmart routing based on location and loadAuto-scaling based on demandA unified API and UI for both automation and accessibilityWhat makes this meaningful isn’t just the tech, it’s the way it vanishes behind the scenes. With Gcore, Mirantis customers can deliver low-latency inference, maximize GPU efficiency, and meet data privacy requirements without touching low-level infrastructure.Many enterprises and cloud customers worry about underutilized GPUs. Now, every cycle is optimized. 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
Run AI inference faster, smarter, and at scale
Training your AI models is only the beginning. The real challenge lies in running them efficiently, securely, and at scale. AI and reality meet in inference—the continuous process of generating predictions in real time. It is the driving force behind virtual assistants, fraud detection, product recommendations, and everything in between. Unlike training, inference doesn’t happen once; it runs continuously. This means that inference is your operational engine rather than just technical infrastructure. And if you don’t manage it well, you’re looking at skyrocketing costs, compliance risks, and frustrating performance bottlenecks. That’s why it’s critical to rethink where and how inference runs in your infrastructure.The hidden cost of AI inferenceWhile training large models often dominates the AI conversation, it’s inference that carries the greatest operational burden. As more models move into production, teams are discovering that traditional, centralized infrastructure isn’t built to support inference at scale.This is particularly evident when:Real-time performance is critical to user experienceRegulatory frameworks require region-specific data processingCompute demand fluctuates unpredictably across time zones and applicationsIf you don’t have a clear plan to manage inference, the performance and impact of your AI initiatives could be undermined. You risk increasing cloud costs, adding latency, and falling out of compliance.The solution: optimize where and how you run inferenceOptimizing AI inference isn’t just about adding more infrastructure—it’s about running models smarter and more strategically. In our new white paper, “How to Optimize AI Inference for Cost, Speed, and Compliance”, we break it down into three key decisions:1. Choose the right stage of the AI lifecycleNot every workload needs a massive training run. Inference is where value is delivered, so focus your resources on where they matter most. Learn when to use pretrained models, when to fine-tune, and when simple inference will do the job.2. Decide where your inference should runFrom the public cloud to on-prem and edge locations, where your model runs, impacts everything from latency to compliance. We show why edge inference is critical for regulated, real-time use cases—and how to deploy it efficiently.3. Match your model and infrastructure to the taskBigger models aren’t always better. We cover how to choose the right model size and infrastructure setup to reduce costs, maintain performance, and meet privacy and security requirements.Who should read itIf you’re responsible for turning AI from proof of concept into production, this guide is for you.Inference is where your choices immediately impact performance, cost, and customer experience, whether you’re managing infrastructure, developing models, or building AI-powered solutions. This white paper will help you cut through complexity and focus on what matters most: running smarter, faster, and more scalable inference.It’s especially relevant if you’re:A machine learning engineer or AI architect deploying models across environmentsA product manager introducing real-time AI featuresA technical leader or decision-maker managing compute, cloud spend, or complianceOr simply trying to scale AI without sacrificing controlIf inference is the next big challenge on your roadmap, this white paper is where to start.Scale AI inference seamlessly with GcoreEfficient, scalable inference is critical to making AI work in production. Whether you’re optimizing for performance, cost, or compliance, you need infrastructure that adapts to real-world demand. Gcore Everywhere Inference brings your models closer to users and data sources—reducing latency, minimizing costs, and supporting region-specific deployments.Our latest white paper, “How to optimize AI inference for cost, speed, and compliance”, breaks down the strategies and technologies that make this possible. From smart model selection to edge deployment and dynamic scaling, you’ll learn how to build an inference pipeline that delivers at scale.Ready to make AI inference faster, smarter, and easier to manage?Download the white paper
How to comply with NIS2: practical tips and key requirements
The European Union is boosting cybersecurity legislation with the introduction of the NIS2 Directive. The new rules represent a significant expansion in how organizations across the continent approach digital security. NIS2 establishes specific and clear expectations that impact not just technology departments but also legal teams and top decision-makers. It refines old protocols while introducing additional obligations that companies must meet to operate within the EU.In this article, we explain the role and scope of the NIS2 Directive, break down its key security requirements, analyze the anticipated business impact, and provide a checklist of actions that businesses can take to remain in compliance with continually evolving regulatory demands.Who needs to comply with NIS2?The NIS2 Directive applies to essential and important organizations operating within the European Union in sectors deemed critical to society and the economy. NIS2 also applies to non-EU companies offering services within the EU, requiring non-EU companies that offer covered services in the EU without a local establishment to appoint a representative in one of the member states where they operate.In general, organizations with 50 or more employees and an annual turnover above €10M fall under NIS2. Smaller entities can also be included if they provide key services, including energy, transport, banking, healthcare, water supply, digital infrastructure, and public administration.4 key security requirements of NIS2Under the NIS2 Directive, organizations are required to have an integrated approach to cybersecurity. There are 10 basic measures that companies subject to this legislation must follow: risk policies, incident handling, supply-chain security, MFA, cryptography, backups, BCP/DRP, vulnerability management, security awareness, crypto-control, and “informational hygiene”. In this article, we will cover the four most important of them.These four are necessary steps for limiting disruptions and achieving full compliance with stringent regulatory demands. They include incident response, risk management, corporate accountability, and reporting obligations.#1 Incident responseUnder NIS2, a solid incident response is required. Companies must document processes for the detection, analysis, and management of cyber incidents. Additionally, organizations must have a trained team ready to respond quickly when there's a breach, reducing damage and downtime. Having the right plan in place can make the difference between a minor issue and a major disruption.#2 Risk managementContinuous risk evaluation is paramount within NIS2. Businesses should constantly be scouting out internal vulnerabilities and external dangers while following a clear, defined risk management protocol. Regular audits and monitoring help businesses stay a step ahead of future threats.#3 Corporate accountabilityNIS2 emphasizes corporate accountability by requiring clear cybersecurity responsibilities across all management levels, placing direct oversight on executive leadership. Additionally, due to the dependency of most organizations on third-party suppliers, supply chain security is paramount. Executives need to check the security measures of their partners. One weak link in the chain can destroy the entire system, making stringent security measures a prerequisite for all partners to reduce risks.#4 Reporting obligationsTransparency lies at the heart of NIS2. Serious incidents need to be reported promptly to maintain the culture of accountability the directive encourages. Good reporting mechanisms ensure that vital information is delivered to the concerned authorities in a timely manner, akin to formal channels in data protection legislation such as the GDPR.What NIS2 means for applicable organizationsSome of the potential implications of NIS2 include an increased regulatory burden, financial and reputational risks, and operational challenges. These apply to all businesses that are already established in the European Union. With compliance now becoming mandatory in all member states, businesses that have lagged behind in implementing effective cybersecurity measures will be put under increased pressure to improve their processes and systems.Increased regulatory burdenFor most firms, the new directive means a huge increase in their regulatory burden. The broadened scope of the directive applies to more industries, and this may lead to additional administrative tasks. Legal personnel and compliance officers will need to sift through current cybersecurity policies and ensure all parts of the organization are in line with the new requirements. This exercise can entail considerable coordination between different departments, including IT, risk management, and supply chain management.Financial and reputational risksThe penalty for non-compliance is steep. The fines for failure to comply with the NIS2 Directive are comparable to the GDPR fines for non-compliance, up to €10 million or 2% of a company's worldwide annual turnover for critical entities, while important organizations face a fine of up to €7M or 1.4% of their global annual turnover. Financial fines and reputational damage are significant risks that organizations must take into account. A single cybersecurity incident can lead to costly investigations, legal battles, and a loss of trust among customers and partners. For companies that depend on digital infrastructure for their day-to-day operations, the cost of non-compliance can be crippling.Operational challengesNIS2 compliance requires more than administrative change. Firms may have to make investments into new technology when trying to meet the directive's requirements, such as expanded monitoring, expanded protection of data, and sophisticated incident response protocols. Legacy system firms can be put at a disadvantage with the need for rapid cybersecurity improvements.NIS2 compliance checklistDue to the comprehensive nature of the NIS2 Directive, organizations will need to adopt a systematic compliance strategy. Here are 5 practical steps organizations can take to comply:Start with a thorough audit. Organizations must review their current cybersecurity infrastructure and identify areas of vulnerability. This kind of audit helps reveal areas of weakness and makes it easier to decide where to invest funds in new tools and training employees.Develop a realistic incident response plan. It is essential to have a short, actionable plan in place when things inevitably go wrong. Organizations need to develop step-by-step procedures for handling breaches and rehearse them through regular training exercises. The plan needs to be constantly updated as new lessons are learned and industry practices evolve.Sustain continued risk management. Risk management is not a static activity. Organizations need to keep their systems safe at all times and update risk analyses from time to time to combat new issues. This allows for timely adjustments to their approach.Check supply chain security. Organizations need to find out how secure their third-party vendors are. They need to have clear-cut security standards and check periodically to help ensure that all members of the supply chain adhere to those standards.Establish clear reporting channels. Organizations must have easy ways of communicating with regulators. They must establish proper reporting schedules and maintain good records. Training reporting groups to report issues early can avoid delays and penalties.Partner with Gcore for NIS2 successGcore’s integrated platform helps organizations address key security concerns relevant to NIS2 and reduce cybersecurity risk:WAAP: Real-time bot mitigation, API protection, and DDoS defense support incident response and ongoing threat monitoring.Edge Cloud: Hosted in ISO 27001 and PCI DSS-compliant EU data centers, offering scalable, resilient infrastructure that aligns with NIS2’s focus on operational resilience and data protection.CDN: Provides fast, secure content delivery while improving redundancy and reducing exposure to availability-related disruptions.Integrated ecosystem: Offers unified visibility across services to strengthen risk management and simplify compliance.Our infrastructure emphasizes data and infrastructure sovereignty, critical for EU-based companies subject to local and cross-border data regulation. With fully-owned data centers across Europe and no reliance on third-party hyperscalers, Gcore enables businesses to maintain full control over where and how their data is processed.Explore our secure infrastructure overview to learn how Gcore’s ecosystem can support your NIS2 compliance journey with continuous monitoring and threat mitigation.Please note that while Gcore’s services support many of the directive’s core pillars, they do not in themselves guarantee full compliance.Ready to get compliant?NIS2 compliance doesn’t have to be overwhelming. We offer tailored solutions to help businesses strengthen their security posture, align with key requirements, and prepare for audits.Interested in expert guidance? Get in touch for a free consultation on compliance planning and implementation. We’ll help you build a roadmap based on your current security posture, business needs, and regulatory deadlines.Get a free NIS2 consultation
Securing vibe coding: balancing speed with cybersecurity
Vibe coding has emerged as a cultural phenomenon in 2025 software development. It’s a style defined by coding on instinct and moving fast, often with the help of AI, rather than following rigid plans. It lets developers skip exhaustive design phases and dive straight into building, writing code (or prompting an AI to write it) in a rapid, conversational loop. It has caught on fast and boasts a dedicated following of developers hosting vibe coding game jams.So why all the buzz? For one, vibe coding delivers speed and spontaneity. Enthusiasts say it frees them to prototype at the speed of thought, without overthinking architecture. A working feature can be blinked into existence after a few AI-assisted prompts, which is intoxicating for startups chasing product-market fit. But as with any trend that favors speed over process, there’s a flip side.This article explores the benefits of vibe coding and the cybersecurity risks it introduces, examines real incidents where "just ship it" coding backfired, and outlines how security leaders can keep up without slowing innovation.The upside: innovation at breakneck speedVibe coding addresses real development needs and has major benefits:Allows lightning-fast prototyping with AI assistance. Speed is a major advantage, especially for startups, and allows faster validation of ideas and product-market fit.Prioritizes creativity over perfection, rewarding flow and iteration over perfection.Lowers barriers to entry for non-experts. AI tooling lowers the skill floor, letting more people code.Produces real success stories, like a game built via vibe coding hitting $1M ARR in 17 days.Vibe coding aligns well with lean, agile, and continuous delivery environments by removing overhead and empowering rapid iteration.When speed bites backVibe coding isn’t inherently insecure, but the culture of speed it promotes can lead to critical oversights, especially when paired with AI tooling and lax process discipline. The following real-world incidents aren’t all examples of vibe coding per se, but they illustrate the kinds of risks that arise when developers prioritize velocity over security, skip reviews, or lean too heavily on AI without safeguards. These three cases show how fast-moving or under-documented development practices can open serious vulnerabilities.xAI API key leak (2025)A developer at Elon Musk’s AI company, xAI, accidentally committed internal API keys to a public GitHub repo. These keys provided access to proprietary LLMs trained on Tesla and SpaceX data. The leak went undetected for two months, exposing critical intellectual property until a researcher reported it. The error likely stemmed from fast-moving development where secrets were hardcoded for convenience.Malicious NPM packages (2024)In January 2024, attackers uploaded npm packages like warbeast2000 and kodiak2k, which exfiltrated SSH keys from developer machines. These were downloaded over 1,600 times before detection. Developers, trusting AI suggestions or searching hastily for functionality, unknowingly included these malicious libraries.OpenAI API key abuse via Replit (2024)Hackers scraped thousands of OpenAI API keys from public Replit projects, which developers had left in plaintext. These keys were abused to access GPT-4 for free, racking up massive bills for unsuspecting users. This incident shows how projects with weak secret hygiene, which is a risk of vibe coding, become easy targets.Securing the vibe: smart risk mitigationCybersecurity teams can enable innovation without compromising safety by following a few simple cybersecurity best practices. While these don’t offer 100% security, they do mitigate many of the major vulnerabilities of vibe coding.Integrate scanning tools: Use SAST, SCA, and secret scanners in CI/CD. Supplement with AI-based code analyzers to assess LLM-generated code.Shift security left: Embed secure-by-default templates and dev-friendly checklists. Make secure SDKs and CLI wrappers easily available.Use guardrails, not gates: Enable runtime protections like WAF, bot filtering, DDoS defense, and rate limiting. Leverage progressive delivery to limit blast radius.Educate, don’t block: Provide lightweight, modular security learning paths for developers. Encourage experimentation in secure sandboxes with audit trails.Consult security experts: Consider outsourcing your cybersecurity to an expert like Gcore to keep your app or AI safe.Secure innovation sustainably with GcoreVibe coding is here to stay, and for good reason. It unlocks creativity and accelerates delivery. But it also invites mistakes that attackers can exploit. Rather than fight the vibe, cybersecurity leaders must adapt: automating protections, partnering with devs, and building a culture where shipping fast doesn't mean shipping insecure.Want to secure your edge-built AI or fast-moving app infrastructure? Gcore’s Edge Security platform offers robust, low-latency protection with next-gen WAAP and DDoS mitigation to help you innovate confidently, even at speed. As AI and security experts, we understand the risks and rewards of vibe coding, and we’re ideally positioned to help you secure your workloads without slowing down development.Into vibe coding? Talk to us about how to keep it secure.
Qwen3 models available now on Gcore Everywhere Inference
We’ve expanded our model library for Gcore Everywhere Inference with three powerful additions from the Qwen3 series. These new models bring advanced reasoning, faster response times, and even better multilingual support, helping you power everything from chatbots and coding tools to complex R&D workloads.With Gcore Everywhere Inference, you can deploy Qwen3 models in just three clicks. Read on to discover what makes Qwen3 special, which Qwen3 model best suits your needs, and how to deploy it with Gcore today.Introducing the new Qwen3 modelsQwen3 is the latest evolution of the Qwen series, featuring both dense and Mixture-of-Experts (MoE) architectures. It introduces dual-mode reasoning, letting you toggle between “thinking” and “non-thinking” modes to balance depth and speed:Thinking mode (enable_thinking=True): The model adds a <think>…</think> block to reason step-by-step before generating the final response. Ideal for tasks like code generation or math where accuracy and logic matter.Non-thinking mode (enable_thinking=False): Skips the reasoning phase to respond faster. Best for straightforward tasks where speed is a priority.Model sizes and use casesWith three new sizes available, you can choose the level of performance required for your use case:Qwen3-14B: A 14B parameter model tuned for responsive, multilingual chat and instruction-following. Fast, versatile, and ready for real-time applications with lightning-fast responses.Qwen3-30B-A3B: Built on the Arch-3 backbone, this 30B model offers advanced reasoning and coding capabilities. It’s ideal for applications that demand deeper understanding and precision while balancing performance. It provides high-quality output with faster inference and better efficiency.Qwen3-32B: The largest Qwen3 model yet, designed for complex, high-performance tasks across reasoning, generation, and multilingual domains. It sets a new standard for what’s achievable with Gcore Everywhere Inference, delivering exceptional results with maximum reasoning power. Ideal for complex computation and generation tasks where every detail matters.ModelArchitectureTotal parametersActive parametersContext lengthBest suited forQwen3-14BDense14B14B128KMultilingual chatbots, instruction-following tasks, and applications requiring strong reasoning capabilities with moderate resource consumption.Qwen3-30B-A3BMoE30B3B128KScenarios requiring advanced reasoning and coding capabilities with efficient resource usage; suitable for real-time applications due to faster inference times.Qwen3-32BDense32B32B128KHigh-performance tasks demanding maximum reasoning power and accuracy; ideal for complex R&D workloads and precision-critical applications.How to deploy Qwen3 models with Gcore in just a few clicksGetting started with Qwen3 on Gcore Everywhere Inference is fast and frictionless. Simply log in to the Gcore Portal, navigate to the AI Inference section, and select your desired Qwen3 model. From there, deployment takes just three clicks—no setup scripts, no GPU wrangling, no DevOps overhead. Check out our docs to discover how it works.Deploying Qwen3 via the Gcore Customer Portal takes just three clicksPrefer to deploy programmatically? Use the Gcore API with your project credentials. We offer quick-start examples in Python and cURL to get you up and running fast.Why choose Qwen3 + Gcore?Flexible performance: Choose from three models tailored to different workloads and cost-performance needs.Immediate availability: All models are live now and deployable via portal or API.Next-gen architecture: Dense and MoE options give you more control over reasoning, speed, and output quality.Scalable by design: Built for production-grade performance across industries and use cases.With the latest Qwen3 additions, Gcore Everywhere Inference continues to deliver on performance, scalability, and choice. Ready to get started? Get a free account today to explore Qwen3 and deploy with Gcore in just a few clicks.Sign up free to deploy Qwen3 today
Run AI workloads faster with our new cloud region in Southern Europe
Good news for businesses operating in Southern Europe! Our newest cloud region in Sines, Portugal, gives you faster, more local access to the infrastructure you need to run advanced AI, ML, and HPC workloads across the Iberian Peninsula and wider region. Sines-2 marks the first region launched in partnership with Northern Data Group, signaling a new chapter in delivering powerful, workload-optimized infrastructure across Europe.Strategically positioned in Portugal, Sines-2 enhances coverage in Southern Europe, providing a lower-latency option for customers operating in or targeting this region. With the explosive growth of AI, machine learning, and compute-intensive workloads, this new region is designed to meet escalating demand with cutting-edge GPU and storage capabilities.Built for AI, designed to scaleSines-2 brings with it next-generation infrastructure features, purpose-built for today’s most demanding workloads:NVIDIA H100 GPUs: Unlock the full potential of AI/ML training, high-performance computing (HPC), and rendering workloads with access to H100 GPUs.VAST NFS (file sharing protocol) support: Benefit from scalable, high-throughput file storage ideal for data-intensive operations, research, and real-time AI workflows.IaaS portfolio: Deploy Virtual Machines, manage storage, and scale infrastructure with the same consistency and reliability as in our flagship regions.Organizations operating in Portugal, Spain, and nearby regions can now deploy workloads closer to end users, improving application performance. For finance, healthcare, public sector, and other organisations running sensitive workloads that must stay within a country or region, Sines-2 is an easy way to access state-of-the-art GPUs with simplified compliance. Whether you're building AI models, running simulations, or managing rendering pipelines, Sines-2 offers the performance and proximity you need.And best of all, servers are available and ready to deploy today.Run your AI workloads in Portugal todayWith Sines-2 and our partnership with Northern Data Group, we’re making it easier than ever for you to run AI workloads at scale. If you need speed, flexibility, and global reach, we’re ready to power your next AI breakthrough.Unlock the power of Sines-2 today
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