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- March 31, 2021
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Introducing Gcore Everywhere AI: 3-click AI training and inference for any environment
For enterprises, telcos, and CSPs, AI adoption sounds promising…until you start measuring impact. Most projects stall or even fail before ROI starts to appear. ML engineers lose momentum setting up clusters. Infrastructure teams battle to balance performance, cost, and compliance. Business leaders see budgets rise while value stays locked in prototypes.Gcore Everywhere AI changes that. It simplifies AI training, deployment, and scaling across on-premises, hybrid, and cloud environments, giving every team the speed, reliability, and control they need to turn AI initiatives into real outcomes.Why we built Everywhere AIEnterprises need AI that runs where it makes the most sense: on-premises for privacy, in the cloud for scale, or across both for hybrid agility. Not all enterprises are “AI-ready”, meaning that for many, the complexity of integrating AI offsets its benefits. We noticed that fragmented toolchains, complex provisioning, and compliance overhead can hinder the value of AI adoption.That’s why we built Everywhere AI: to simplify deployment, orchestration, and scaling for AI workloads across any environment, all controlled in one intuitive platform. We’re on a mission to bring every enterprise, CSP, and telco team a consistent, secure, and simple way to make AI efficient—everywhere.There are many tools on the market that promise to deliver similar benefits, but no other is able to simplify the deployment process to the point where it’s accessible to anyone in the business, regardless of their technical expertise. To use Everywhere AI, you don’t need to have a Ph.D. in Machine Learning or be a seasoned infrastructure engineer. Everywhere AI is for everyone at your organization.Enterprises today need AI that simply works, whether on-premises, in the cloud, or in hybrid deployments. With Everywhere AI, we’ve taken the complexity out of AI deployment, giving customers an easier, faster way to deploy high-performance AI with a streamlined user experience, stronger ROI, and simplified compliance across environments. This launch is a major step toward our goal at Gcore to make enterprise-grade AI accessible, reliable, and performant.Seva Vayner, Product Director of Edge Cloud and AI at GcoreFeatures and benefitsEverywhere AI brings together everything needed to train, deploy, and scale AI securely and efficiently:Deploy in just 3 clicks: Move from concept to training in minutes using JupyterLab or Slurm. Or simply select your tool, cluster size, and location, and let Everywhere AI handle your setup, orchestration, and scaling automatically.Unified control plane: Manage training, inference, and scaling from one dashboard, across on-prem, hybrid, and cloud. Operate in public or private clouds, or in fully air-gapped environments when data can’t leave your network.Gcore Smart Routing: Inference requests automatically reach the nearest compliant GPU region for ultra-low latency and simplified regulatory alignment. Built on Gcore’s global edge network (210+ PoPs), Smart Routing delivers uncompromising performance worldwide.Auto-scaling: Handle demand spikes seamlessly. Scale to zero when idle to reduce costs, or burst instantly for inference peaks.Privacy and sovereignty: Designed for regulated industries, Everywhere AI supports hard multitenancy for project isolation and sovereign privacy for sensitive workloads. Whether hybrid or fully disconnected, your models stay under your control.Proven resultsEnterprises deploying Everywhere AI can expect to see measurable, repeatable improvements:2× higher GPU utilization: Boost efficiency from ~40% to 80–95% with multi-tenancy and auto-scaling.80% lower infrastructure admin load: Infrastructure teams are more productive with automated software rollout and updates.From POC to results in one week: Enterprise teams take less than a week to onboard, test, and start seeing performance improvements from Everywhere AI.Early adopters are already validating Everywhere AI’s performance and flexibility.Gcore Everywhere AI and HPE GreenLake streamlines operations by removing manual provisioning, improving GPU utilization, and meeting application requirements, including fully air-gapped environments and ultra-low latency. By simplifying AI deployment and management, we’re helping enterprises deliver AI faster and create applications that deliver benefits regardless of scale: good for ML engineers, infrastructure teams, and business leaders.Vijay Patel, Global Director Service Providers and Co-Location Business, HPEPurpose-built for regulated industriesEverywhere AI is designed for organizations where privacy, uptime, and compliance are non-negotiable.Telcos: Use CDN-integrated Smart Routing to deliver real-time inference at carrier scale with consistent QoS.Finance firms: Deploy risk and fraud prevention models on-premises for data residency while scaling, benefiting from auto-scaling and multi-tenancy for maximum efficiency.Healthcare providers: Run imaging and diagnostics AI inside hospital networks to protect PHI.Public-sector agencies: Deliver robust AI-driven citizen services securely under strict compliance regimes.Industrial enterprises: Leverage model and GPU health checks on edge deployments to keep critical predictive maintenance models running in remote sites.Run AI on your termsWhether you’re training large models on-premises, scaling inference at the edge, or operating across multiple regions, Gcore Everywhere AI gives you full control over performance, cost, and compliance.Ready to deploy AI everywhere you need it? Discover how Everywhere AI can simplify and accelerate your AI operations.Learn more about Everywhere AI
Introducing AI Cloud Stack: turning GPU clusters into revenue-generating AI clouds
Enterprises and cloud providers face major roadblocks when trying to deploy GPU infrastructure at scale: long time-to-market, operational inefficiencies, and difficulty bringing new capacity to market profitably. Establishing AI environments with hyperscaler-grade functionality typically requires years of engineering effort, multiple partner integrations, and complex operational tooling.Not anymore.With Gcore AI Cloud Stack, organizations can transform bare Nvidia GPU clusters into a fully cloud-enabled environment—complete with orchestration, observability, billing, and go-to-market support—all in a fraction of the time it would take to build from scratch, maximizing GPU utilization.This proven solution marks the latest addition to the Gcore AI product suite, enabling enterprises and cloud providers to accelerate AI cloud deployment through better GPU utilization, monetization, reduced complexity, and hyperscaler-grade functionality in their own AI environments. Gcore AI Cloud Stack is already powering leading technology providers, including VAST and Nokia.Why we built AI Cloud StackBuying and efficiently operating GPUs at a large scale requires significant investment, time, and expertise. Most organizations need to hit the ground running, bypassing years of in-house R&D. Without a robust reference architecture, infrastructure and network preparation, 24/7 monitoring, dynamic resource allocation, orchestration abstraction, and clear paths to utilization or commercialization, enterprises can spend years before seeing ROI.“Gcore brings together the key pieces—compute, networking, and storage—into a usable stack. That integration helps service providers stand up AI clouds faster and onboard clients sooner, accelerating time to revenue. Combined with the advanced multi-tenant capabilities of VAST’s AI Operating System, it delivers a reliable, scalable, and futureproof AI infrastructure. Gcore offers operators a valuable option to move quickly without building everything themselves.”— Dan Chester, CSP Director EMEA, VAST DataAt Gcore, we understand that organizations across industries will continue to invest heavily in GPUs to power the next wave of AI innovation—meaning these challenges aren’t going away. AI Cloud Stack solves today’s challenges and anticipates tomorrow’s. It ensures that GPU infrastructure at the core of AI innovation delivers maximum value to enterprises.How AI Cloud Stack worksThis comprehensive solution is structured across three stages.1. Provision and launchGcore handles the complexities of initial deployment, from physical infrastructure setup to orchestration, enabling enterprises to go live quickly with a reliable GPU cloud.2. Operations and managementThe solution includes monitoring, orchestration, ticket management, and ongoing support to keep environments stable, secure, and efficient. This includes automated GPU failure handling and optimized resource management.3. Go-to-market supportUnlike other solutions, AI Cloud Stack goes beyond infrastructure. Building on Gcore’s experience as a trusted NVIDIA Cloud Provider (NCP), it helps customers sell their capacity, including through established reseller channels. This integrated GTM support ensures capacity doesn’t sit idle, losing value and potential.What sets Gcore apartUnlike many providers entering this market, Gcore has operated as a global cloud provider for over a decade and has been an early player in the global AI landscape. Gcore knows what it takes to build, scale, and sell cloud and AI services—because it has done it for customers and partners worldwide. Gcore AI Cloud Stack has already been deployed on thousands of NVIDIA Hopper GPUs across Europe to build a commercial-grade AI cloud with full orchestration, abstraction, and monetization layers. That real-world experience allows Gcore to deliver the infrastructure, operational playbook, and sales enablement customers need to succeed.“We’re pleased to collaborate with Gcore, a strong European ISV, to advance a networking reference architecture for AI clouds. Combining Nokia’s open, programmable, and reliable networking with Gcore’s cloud software accelerates deployable blueprints that customers can adopt across data centers and the edge.”— Mark Vanderhaegen, Head of Business Development, Data Center Networks, NokiaKey features of AI Cloud StackCloudification of GPU clusters: Transform raw infrastructure into cloud-like consumption: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), GPU as a Service (GPUaaS), or Model as a Service (MaaS).Gcore AI suite integration: Enable serverless inference and training capabilities through Gcore’s enterprise AI suite.Hyperscaler functionality: Built-in billing, observability, orchestration, and professional services deliver the tools CSPs and enterprises need to operate—similar to what they’re used to getting on public cloud.White-label options: Deliver capacity under your own brand while relying on Gcore’s proven global cloud backbone.NVIDIA AI Enterprise-ready: Integrate pretrained models, chatbots, and NVIDIA AI blueprints to accelerate time-to-market.The future of AI cloudsWith Gcore AI Cloud Stack, enterprises no longer need to spend years building the operational, technical, and commercial capabilities required to utilize and monetize GPU infrastructure. Instead, they can launch in a few months with a hyperscaler-grade solution designed for today’s AI demands.Whether you’re a cloud service provider, an enterprise investing in AI infrastructure, or a partner looking to accelerate GPU monetization, AI Cloud Stack gives you the speed, scalability, and GTM support you need.Ready to turn your GPU clusters into a fully monetized, production-grade AI cloud? Talk with our AI experts to learn how you can go from bare metal to model-as-a-service in months, not years.Get a customized consultation
Edge AI is your next competitive advantage: highlights from Seva Vayner’s webinar
Edge AI isn’t just a technical milestone. It’s a strategic lever for businesses aiming to gain a competitive advantage with AI.As AI deployments grow more complex and more global, central cloud infrastructure is hitting real-world limits: compliance barriers, latency bottlenecks, and runaway operational costs. The question for businesses isn’t whether they’ll adopt edge AI, but how soon.In a recent webinar with Mobile World Live, Seva Vayner, Gcore’s Product Director of Edge Cloud and AI, made the business case for edge inference as a competitive differentiator. He outlined what it takes to stay ahead in a world where speed, locality, and control define AI success.Scroll on to watch Seva explain why your infrastructure choices now shape your market position later.Location is everything: edge over cloudAI is no longer something globally operating businesses can afford to run from a central location. Regional regulations and growing user expectations mean models must be served as close to the user as possible. This reduces latency, but perhaps more importantly is essential for compliance with local laws.Edge AI also keeps costs down by avoiding costly international traffic routes. When your users are global but your infrastructure isn’t, every request becomes an expensive, high-latency journey across the internet.Edge inference solves three problems at once in an increasingly regionally fragmented AI landscape:Keeps compute near users for low latencyCuts down on international transit for reduced costsHelps companies stay compliant with local lawsPrivate edge: control over convenienceMany businesses started their AI journey by experimenting with public APIs like OpenAI’s. But as companies and their AI use cases mature, that’s not good enough anymore. They need full control over data residency, model access, and deployment architecture, especially in regulated industries or high-sensitivity environments.That’s where private edge deployments come in. Instead of relying on public endpoints and shared infrastructure, organizations can fully isolate their AI environments, keeping data secure and models proprietary.This approach is ideal for healthcare, finance, government, and any sector where data sovereignty and operational security are critical.Optimizing edge AI: precision over powerDeploying AI at the edge requires right-sizing your infrastructure for the models and tasks at hand. That’s both technically smarter and far more cost-effective than throwing maximum power and size at every use case.Making smart trade-offs allows businesses to scale edge AI sustainably by using the right hardware for each use case.AI at the edge helps businesses deliver the experience without the excess. With the control that the edge brings, hardware costs can be cut by using exactly what each device or location requires, reducing financial waste.Final takeawayAs Seva put it, AI infrastructure decisions are no longer just financial; they’re part of serious business strategy. From regulatory compliance to operational cost to long-term scalability, edge inference is already a necessity for businesses that plan to serve AI at scale and get ahead in the market.Gcore offers a full suite of public and private edge deployment options across six continents, integrated with local telco infrastructure and optimized for real-time performance. Learn more about Everywhere Inference, our edge AI solution, or get in touch to see how we can help tailor a deployment model to your needs.Ready to get started? Deploy a model in just three clicks with Gcore Everywhere Inference.Discover Everywhere Inference
From budget strain to AI gain: Watch how studios are building smarter with AI
Game development is in a pressure cooker. Budgets are ballooning, infrastructure and labor costs are rising, and players expect more complexity and polish with every release. All studios, from the major AAAs to smaller indies, are feeling the strain.But there is a way forward. In a recent webinar, Sean Hammond, Territory Manager for the UK and Nordics at Gcore, explained how AI is reshaping game development workflows and how the right infrastructure strategy can reduce costs, speed up production, and create better player experiences.Scroll on to watch key moments from Sean's talk and explore how studios can make AI work for them.Rising costs are threatening game developmentGame revenue has slowed, but development costs continue to rise. Some AAA titles now surpass $100 million in development budgets. The complexity of modern games demands more powerful servers, scalable infrastructure, and larger teams, making the industry increasingly unsustainable.Personnel and infrastructure costs are also climbing. Developers, artists, and QA testers with specialized skills are in high demand, as are technologies like VR, AR, and AI. Studios are also having to invest more in cybersecurity to protect player data, detect cheating, and safeguard in-game economies.AI is revolutionizing GameDev, even without a perfect use caseWhile the perfect use case for AI in gaming may not have been found yet, it’s already transforming how games are built, tested, and personalized.Sean highlighted emerging applications, including:Smarter QA testingAI-driven player personalizationReal-time motion and animationAccelerated environment and character designMultilingual localizationAdaptive game balancingStudios are already applying these technologies to reduce production timelines and improve immersion.The challenge of secure, scalable AI adoptionOf course, AI adoption doesn’t come without its challenges. Chief among them is security. Public models pose risks: no studio wants their proprietary assets to end up training a competitor’s model.The solution? Deploy AI models on infrastructure you trust so you’re in complete control. That’s where Gcore comes in.Gcore Everywhere Inference reduces compute costs and infrastructure bloat by allowing you to deploy only what you need, where you need it.The future of gaming is AI at scaleTo power real-time player experiences, your studio needs to deploy AI globally, close to your users.Gcore Everywhere Inference lets you deploy models worldwide at the edge with minimal latency because data is not routed back to central servers. This means fast, responsive gameplay and a new generation of real-time, AI-driven features.As a company originally built by gamers, we’ve developed AI solutions with gaming studios in mind. Here’s what we offer:Global edge inference for real-time gameplay: Deploy your AI models close to players worldwide, enabling fast, responsive player experiences without routing data to central servers.Full control over AI model deployment and IP protection: Avoid public APIs and retain full ownership of your assets with on-prem options, preventing your proprietary data from being available to competitors.Scalable, cost-efficient infrastructure tailored to gaming workloads: Deploy only what you need to avoid overprovisioning and reduce compute costs without sacrificing performance.Enhanced player retention through AI-driven personalization and matchmaking: Real-time inference powers smarter NPCs and dynamic matchmaking, improving engagement and keeping players coming back for more.Deploy models in 3 clicks and under 10 seconds: Our developer-friendly platform lets you go from trained model to global deployment in seconds. No complex DevOps setup required.Final takeawayAI is advancing game development fast, but only if it’s deployed right. Gcore offers scalable, secure, and cost-efficient AI infrastructure that helps studios create smarter, faster, and more immersive games.Want to see how it works? Deploy your first model in just a few clicks.Check out our blog on how AI is transforming gaming in 2025
How AI-enhanced content moderation is powering safe and compliant streaming
As streaming experiences a global boom across platforms, regions, and industries, providers face a growing challenge: how to deliver safe, respectful, and compliant content delivery at scale. Viewer expectations have never been higher, likewise the regulatory demands and reputational risks.Live content in particular leaves little room for error. A single offensive comment, inappropriate image, or misinformation segment can cause long-term damage in seconds.Moderation has always been part of the streaming conversation, but tools and strategies are evolving rapidly. AI-powered content moderation is helping providers meet their safety obligations while preserving viewer experience and platform performance.In this article, we explore how AI content moderation works, where it delivers value, and why streaming platforms are adopting it to stay ahead of both audience expectations and regulatory pressures.Real-time problems require real-time solutionsHuman moderators can provide accuracy and context, but they can’t match the scale or speed of modern streaming environments. Live streams often involve thousands of viewers interacting at once, with content being generated every second through audio, video, chat, or on-screen graphics.Manual review systems struggle to keep up with this pace. In some cases, content can go viral before it is flagged, like deepfakes that circulated on Facebook leading up to the 2025 Canadian election. In others, delays in moderation result in regulatory penalties or customer churn, like X’s 2025 fine under the EU Digital Services Act for shortcomings in content moderation and algorithm transparency. This has created a demand for scalable solutions that act instantly, with minimal human intervention.AI-enhanced content moderation platforms address this gap. These systems are trained to identify and filter harmful or non-compliant material as it is being streamed or uploaded. They operate across multiple modalities—video frames, audio tracks, text inputs—and can flag or remove content within milliseconds of detection. The result is a safer environment for end users.How AI moderation systems workModern AI moderation platforms are powered by machine learning algorithms trained on extensive datasets. These datasets include a wide variety of content types, languages, accents, dialects, and contexts. By analyzing this data, the system learns to identify content that violates platform policies or legal regulations.The process typically involves three stages:Input capture: The system monitors live or uploaded content across audio, video, and text layers.Pattern recognition: It uses models to identify offensive content, including nudity, violence, hate speech, misinformation, or abusive language.Contextual decision-making: Based on confidence thresholds and platform rules, the system flags, blocks, or escalates the content for review.This process is continuous and self-improving. As the system receives more inputs and feedback, it adapts to new forms of expression, regional trends, and platform-specific norms.What makes this especially valuable for streaming platforms is its low latency. Content can be flagged and removed in real time, often before viewers even notice. This is critical in high-stakes environments like esports, corporate webinars, or public broadcasts.Multi-language moderation and global streamingStreaming audiences today are truly global. Content crosses borders faster than ever, but moderation standards and cultural norms do not. What’s considered acceptable in one region may be flagged as offensive in another. A word that is considered inappropriate in one language might be completely neutral in another. A piece of nudity in an educational context may be acceptable, while the same image in another setting may not be. Without the ability to understand nuance, AI systems risk either over-filtering or letting harmful content through.That’s why high-quality moderation platforms are designed to incorporate context into their models. This includes:Understanding tone, not just keywordsRecognizing culturally specific gestures or idiomsAdapting to evolving slang or coded languageApplying different standards depending on content type or target audienceThis enables more accurate detection of harmful material and avoids false positives caused by mistranslation.Training AI models for multi-language support involves:Gathering large, representative datasets in each languageTeaching the model to detect content-specific risks (e.g., slurs or threats) in the right cultural contextContinuously updating the model as language evolvesThis capability is especially important for platforms that operate in multiple markets or support user-generated content. It enables a more respectful experience for global audiences while providing consistent enforcement of safety standards.Use cases across the streaming ecosystemAI moderation isn’t just a concern for social platforms. It plays a growing role in nearly every streaming vertical, including the following:Live sports: Real-time content scanning helps block offensive chants, gestures, or pitch-side incidents before they reach a wide audience. Fast filtering protects the viewer experience and helps meet broadcast standards.Esports: With millions of viewers and high emotional stakes, esports platforms rely on AI to remove hate speech and adult content from chat, visuals, and commentary. This creates a more inclusive environment for fans and sponsors alike.Corporate live events: From earnings calls to virtual town halls, organizations use AI moderation to help ensure compliance with internal communication guidelines and protect their reputation.Online learning: EdTech platforms use AI to keep classrooms safe and focused. Moderation helps filter distractions, harassment, and inappropriate material in both live and recorded sessions.On-demand entertainment: Even outside of live broadcasts, moderation helps streaming providers meet content standards and licensing obligations across global markets. It also ensures user-submitted content (like comments or video uploads) meets platform guidelines.In each case, the shared goal is to provide a safe and trusted streaming environment for users, advertisers, and creators.Balancing automation with human oversightAI moderation is a powerful tool, but it shouldn’t be the only one. The best systems combine automation with clear review workflows, configurable thresholds, and human input.False positives and edge cases are inevitable. Giving moderators the ability to review, override, or explain decisions is important for both quality control and user trust.Likewise, giving users a way to appeal moderation decisions or report issues ensures that moderation doesn’t become a black box. Transparency and user empowerment are increasingly seen as part of good platform governance.Looking ahead: what’s next for AI moderationAs streaming becomes more interactive and immersive, moderation will need to evolve. AI systems will be expected to handle not only traditional video and chat, but also spatial audio, avatars, and real-time user inputs in virtual environments.We can also expect increased demand for:Personalization, where viewers can set their own content preferencesIntegration with platform APIs for programmatic content governanceCross-platform consistency to support syndicated content across partnersAs these changes unfold, AI moderation will remain central to the success of modern streaming. Platforms that adopt scalable, adaptive moderation systems now will be better positioned to meet the next generation of content challenges without compromising on speed, safety, or user experience.Keep your streaming content safe and compliant with GcoreGcore Video Streaming offers AI Content Moderation that satisfies today’s digital safety concerns while streamlining the human moderation process.To explore how Gcore AI Content Moderation can transform your digital platform, we invite you to contact our streaming team for a demonstration. Our docs provide guidance for using our intuitive Gcore Customer Portal to manage your streaming content. We also provide a clear pricing comparison so you can assess the value for yourself.Embrace the future of content moderation and deliver a safer, more compliant digital space for all your users.Try AI Content Moderation for freeTry AI Content Moderation for free
Deploy GPT-OSS-120B privately on Gcore
OpenAI’s release of GPT-OSS-120B is a turning point for LLM developers. It’s a 120B parameter model trained from scratch, licensed for commercial use, and available with open weights. This is a serious asset for serious builders.Gcore now supports private GPT-OSS-120B deployments via our Everywhere Inference platform. That means you can stand up your own endpoint in minutes, run inference at scale, and control the full stack, without API limits, vendor lock-in, or hidden usage fees. Just fast, secure, controlled deployment on your terms. Deploy now in three clicks or read on to learn more.Why GPT-OSS-120B is big news for buildersThis model changes the game for anyone developing AI apps, platforms, or infrastructure. It brings GPT-3-level reasoning to the open-source ecosystem and frees developers from closed APIs.With GPT-OSS-120B, you get:Full access to model weights and architectureSelf-hosting for maximum data control and privacySupport for fine-tuning and model editingOffline deployment for secure or air-gapped useMassive cost savings at scaleYou can deploy in any Gcore region (or leverage Gcore’s three-click serverless inference on your own infrastructure), route traffic through your own stack, and fully control load, latency, and logs. This is LLM deployment for real-world apps, not just playground prompts.How to deploy GPT-OSS-120B with Gcore Everywhere InferenceGcore Everywhere Inference gives you a clean path from open model to production endpoint. You can spin up a dedicated deployment in just three clicks. We offer configuration options to suit your business needs:Choose your location (cloud or on-prem)Integrate via standard APIs (OpenAI-compatible)Control usage, autoscale, and costsDeploying GPT-OSS-120B on Gcore takes just three clicks in the Gcore Customer Portal.There are no shared endpoints. You get dedicated compute, low-latency routing, and full control and observability.You can also bring your own trained variant if you’ve fine-tuned GPT-OSS-120B elsewhere. We’ll help you host it reliably, close to your users.Use cases: where GPT-OSS-120B fits bestCommercial GPTs still outperform OSS models on some general tasks, but GPT-OSS-120B gives you control, portability, and flexibility where it counts. Most importantly, it gives you the ability to build privacy-sensitive applications.Great fits include:Internal dev tools and copilotsRetrieval-augmented generation (RAG) pipelinesSecure, private enterprise assistantsData-sensitive, on-prem AI workloadsModels requiring full customization or fine-tuningIt’s especially relevant for finance, healthcare, government, and legal teams operating under strict compliance rules.Deploy GPT-OSS-120B todayWant to learn more about GPT-OSS-120B and why Gcore is an ideal provider for deployment? Get all the information you need on our dedicated page.And if you’re ready to deploy in just three clicks, head on over to the Gcore Customer Portal. GPT-OSS-120B is waiting for you in the Application Catalog.Learn more about deploying GPT-OSS-120B on Gcore
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