Training AI models has become an incredibly resource-intensive endeavor, requiring immense computational power and a significant investment in expertise. This complexity has created a barrier that only a handful of companies can realistically overcome. Tech giants are leading the charge in training large-scale foundational models, leveraging their unmatched resources and specialized teams.
For most other organizations, the focus in 2025 will shift to fine-tuning and inference. Rather than starting from scratch, businesses can adapt pretrained models to suit their specific needs, making AI more accessible and cost-effective. This approach allows companies to harness the power of AI without needing to build extensive infrastructure or assemble teams of highly specialized researchers. By narrowing the scope to fine-tuning and inference, businesses can remain competitive in an AI-driven world without shouldering the immense costs and challenges of model training.
Inference on the rise: predictions for 2025
As AI embeds itself deeper into the fabric of various industries, inference is taking center stage as the key to unlocking real-time, actionable insights. Its importance is growing rapidly, driven by the need for faster decision-making, the proliferation of connected devices, and the push toward decentralized computing. By 2025, inference will be a cornerstone of AI applications, with several trends defining its trajectory.
One notable shift is the increasing demand for instant insights across sectors like retail, finance, and healthcare. Businesses are now relying on AI systems that can analyze data and generate responses in milliseconds. For example, customer service chatbots must provide quick, coherent answers to maintain user satisfaction, while predictive maintenance systems in manufacturing need to process sensor data immediately to preempt equipment failures. The emphasis on speed and precision is reshaping how inference engines are designed and deployed.
The explosion of Internet of Things (IoT) devices is another factor accelerating the rise of inference. These devices, from industrial sensors to connected home gadgets, produce a continuous stream of data. In sectors like transportation, AI-powered traffic management systems rely on real-time analysis of this data to adjust signals dynamically and reduce congestion. The sheer scale of these applications demands robust inference capabilities that can handle data flows without delays, providing optimal performance.
Edge inference is emerging as a transformative solution to meet these demands. By processing data closer to its source, edge computing eliminates the delays caused by sending information to centralized data centers. This localized approach not only reduces latency but also lowers bandwidth costs and enhances data security by keeping sensitive information on-site. It is especially critical for time-sensitive applications like autonomous vehicles, where even a split-second delay could result in catastrophic outcomes, or in healthcare, where real-time patient monitoring can be life-saving.
Inference is evolving from a technical process into a strategic advantage, enabling businesses to deliver faster, smarter, and more secure AI solutions. As it becomes integral to everything from customer interactions to operational efficiency, its role in shaping the future of AI cannot be overstated. In 2025 and beyond, inference will not just support AI applications; it will drive them.
Real-world use cases
Inference is already transforming industries, and its potential will only grow. Consider Walmart’s efforts in “adaptive retail,” an initiative to create highly personalized shopping experiences. By integrating AI into its operations, Walmart can deliver tailored offers, enhance fraud detection, and improve store efficiency—all powered by real-time inference.
Similar advancements are occurring across industries:
- Customer experience: AI inference enables instant translations, hyper-personalized recommendations, and seamless chatbot interactions, enhancing customer engagement.
- Operational optimization: Inference drives predictive maintenance, process automation, and demand forecasting, reducing costs and improving efficiency.
- Risk mitigation: From fraud detection to cybersecurity, AI’s ability to analyze threats in real time helps businesses stay ahead of potential risks.
The edge advantage in AI inference
The rise of edge computing is tightly interwoven with the growing importance of inference. By processing data closer to the source, edge technology provides four major benefits:
- Ultra-low latency: Inference applications like virtual assistants, self-driving cars, and smart home systems demand split-second decision-making. Edge computing eliminates the need to send data to centralized servers, reducing delays and providing smooth operation.
- Scalability for high-demand scenarios: In industries like retail or streaming, demand for AI services can spike unexpectedly. Edge computing allows workloads to be distributed across multiple nodes, keeping systems responsive during peak times. For example, a live sports broadcast with millions of viewers can use edge inference to analyze viewer preferences and deliver personalized ad content in real time.
- Enhanced security and compliance: For industries like healthcare and finance, where data privacy is critical, edge inference offers a safer alternative by keeping sensitive data on-site. This reduces the risk of breaches while promoting compliance with regional data protection regulations.
- Cost efficiency: Beyond low latency and security, edge computing significantly reduces costs by minimizing data transfer and central server reliance.
Preparing for the era of inference
As AI continues to evolve, businesses must make strategic decisions about their AI infrastructure. The ability to deliver low-latency, scalable, and secure inference applications will determine success in the years ahead. Here are some practical steps to prepare:
- Adopt edge technology: Partner with cloud providers offering robust edge capabilities to make sure your AI inference infrastructure meets performance demands.
- Optimize AI models: Focus on fine-tuning pretrained models for your specific needs rather than attempting full-scale training.
- Prioritize compliance: Check your AI systems adhere to regional data laws, especially if operating across borders.
Inference computing for 2025 and beyond, powered by Gcore
Gcore is a leading provider of the type of future-proofed edge infrastructure needed to shepherd inference computing use cases into 2025. Our expansive content delivery network (CDN) platform supports real-time inference for AI applications like customer service chatbots, fraud detection, and recommender systems, delivering low-latency responses. Gcore has the edge computing power, global reach, and robust security to help companies deploy and scale AI models on a flexible, powerful infrastructure.
At Gcore, we recently announced an enormous boost to our Edge AI capacity with our Finland-based NVIDIA H100 GPU cluster and new cutting-edge GPUs: NVIDIA GB200 and H200. With next-gen scalability, speed, and efficiency, these advances set the stage for groundbreaking AI advancements for our customers.
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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
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
How AI is transforming gaming experiences
AI is reshaping how games are played, built, and experienced. Although we are in a period of flux where the optimal combination of human and artificial intelligence is still being ironed out, the potential for AI to greatly enhance both gameplay and development is clear.PlayStation CEO Hermen Hulst recently emphasized the importance of striking the right balance between the handcrafted human touch and the revolutionary advances that AI brings. AI will not replace the decades of design, storytelling, and craft laid down by humans—it will build on that foundation to unlock entirely new possibilities. In addition to an enhanced playing experience, AI is shaking up gaming aspects such as real-time analytics, player interactions, content generation, and security.In this article, we explore three specific developments that are enriching gaming storyworlds, as well as the technology that’s bringing them to life and what the future might hold.#1 Responsive NPC behavior and smarter opponentsAI is evolving to create more realistic, adaptive, and intelligent non-player characters (NPCs) that can react to individual player choices with greater depth and reasoning. The algorithms allow NPCs to respond dynamically to players’ decisions so they can adjust their strategies and behaviors in real time. This provides a more immersive and dynamic gameplay environment and means gamers have endless opportunities to experience new gaming adventures and write their own story every time.A recent example is Red Dead Redemption 2, which enables players to interact with NPCs in the Wild West. Players were impressed by its complexity and the ability to interact with fellow cowboys and bar patrons. Although this is limited for now, eventually, it could become like a video game version of the TV series Westworld, in which visitors pay to interact with incredibly lifelike robots in a Wild West theme park.AI also gives in-game opponents more “agency,” making them more reactive and challenging for players to defeat. This means smarter, more unpredictable enemies who provide a heightened level of achievement, novelty, and excitement for players.For example, AI Limit, released in early 2025, is an action RPG incorporating AI-driven combat mechanics. While drawing comparisons to Soulslike games, the developers emphasize its unique features, including the Sync Rate system, which adds depth to combat interactions.#2 AI-assisted matchmaking and player behavior predictionsAI-powered analytics can identify and predict player skill levels and playing styles, leading to more balanced and competitive matchmaking. A notable example is the implementation of advanced matchmaking systems in competitive games such as Apex Legends and Call of Duty: Modern Warfare III. These titles use AI algorithms to analyze not just skill levels but also playstyle preferences, weapon selections, and playing patterns to create matches optimized for player retention and satisfaction. The systems continuously learn from match outcomes to predict player behavior and create more balanced team compositions across different skill levels.By analyzing a player’s past performance, AI can also create smarter team formations. This makes for fairer and more rewarding multiplayer games, as players are matched with others who complement their skill and strategy.AI can monitor in-game interactions to detect and mitigate toxic behavior. This helps encourage positive social dynamics and foster a more collaborative and friendly online environment.#3 Personalized gaming experiencesMultiplayer games can use AI to analyze player behavior in real time, adjusting difficulty levels and suggesting tailored missions, providing rich experiences unique to each player. This creates personalized, player-centric gameplay that evolves dynamically and can change over time as a player’s knowledge and ability improve.Games like Minecraft and Skyrim already use AI to adjust difficulty and offer dynamic content, while Oasis represents a breakthrough as an AI-generated Minecraft-inspired world. The game uses generative AI to predict and render gameplay frames in real time, creating a uniquely responsive environment.Beyond world generation, modern games are also incorporating AI chatbots that give players real-time coaching and personalized skill development tips.How will AI continue to shape gaming?In the future, AI will continue to impact not just the player experience but also the creation of games. We anticipate AI revolutionizing game development in the following areas:Procedural content generation: AI will create vast, dynamic worlds or generate storylines, allowing for more expansive and diverse game worlds than are currently available.Game testing: AI will simulate millions of player interactions to help developers find bugs and improve gameplay.Art and sound design: AI tools will be used to a greater extent than at present to create game art, music, and voiceovers.How Gcore technology is powering AI gaming innovationIn terms of the technology behind the scenes, Gcore Everywhere Inference brings AI models closer to players by deploying them at the edge, significantly reducing latency for training and inference. This powers dynamic features like adaptive NPC behavior, personalized gameplay, and predictive matchmaking without sacrificing performance.Gcore technology differentiates itself with the following features:Supports all major frameworks, including PyTorch, TensorFlow, ONNX, and Hugging Face Transformers, making deploying your preferred model architecture easy.Offers multiple deployment modes, whether in the cloud, on-premise, or across our distributed edge network with 180+ global locations, allowing you to place inference wherever it delivers the best performance for your users.Delivers sub-50ms latency for inference workloads in most regions, even during peak gaming hours, thanks to our ultra-low-latency CDN and proximity to players.Scales horizontally, so studios can support millions of concurrent inferences for dynamic NPC behavior, matchmaking decisions, or in-game voice/chat moderation, without compromising gameplay speed.Keeps your models and training data private through confidential computing and data sovereignty controls, helping you meet compliance requirements across regions including Europe, LATAM, and MENA.With a low-latency infrastructure that supports popular AI frameworks, Gcore Everywhere Inference allows your studio to deploy custom models and deliver more immersive, responsive player experiences at scale. With our confidential computing solutions, you retain full control over your training assets—no data is shared, exposed, or compromised.Deliver next-gen gaming with Gcore AIAI continues to revolutionize industries, and gaming is no exception. The deployment of artificial intelligence can help make games even more exciting for players, as well as enabling developers to work smarter when creating new games.At Gcore, AI is our core and gaming is our foundation. AI is seamlessly integrated into all our solutions with one goal in mind: to help grow your business. As AI continues to evolve rapidly, we're committed to staying at the cutting edge and changing with the future. Contact us today to discover how Everywhere Inference can enhance your gaming offerings.Get a customized consultation about AI gaming deployment
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