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News

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

November 3, 2025 3 min read

News

Gcore partners with AVEQ to elevate streaming performance monitoring

At Gcore, delivering exceptional streaming experiences to users across our global network is at the heart of what we do. We're excited to share how we're taking our CDN performance monitoring to new heights through our partnership with AVEQ and their innovative Surfmeter solution.Operating a massive global network spanning 210 points of presence across six continents comes with unique challenges. While our globally distributed caching infrastructure already ensures optimal performance for end-users, we recognized the need for deeper insights into the complex interactions between applications and our underlying network. We needed to move beyond traditional server-side monitoring to truly understand what our customers' users experience in the real world.Real-world performance visibilityThat's where AVEQ's Surfmeter comes in. We're now using Surfmeter to gain unprecedented visibility into our network performance through automated, active measurements that simulate actual streaming video quality, exactly as end-users experience it.This isn't about checking boxes or reviewing server logs. It's about measuring what users see on their screens at home. With Surfmeter, our engineering teams can identify and resolve potential bottlenecks or suboptimal configurations, and collaborate more effectively with our customers to continuously improve Quality of Experience (QoE).How we use SurfmeterAVEQ helps us simulate and analyze real-world scenarios where users access different video streams. Their software runs both on network nodes close to our data center CDN caches and at selected end-user locations with genuine ISP connections.What sets Surfmeter apart is its authentic approach: it opens video streams from the same platforms and players that end-users rely on, ensuring measurements truly represent real-world conditions. Unlike monitoring solutions that simply check stream availability, Surfmeter doesn't make assumptions or use third-party playback engines. Instead, it precisely replicates how video players request and decode data served from our CDN.Rapid issue resolutionWhen performance issues arise, Surfmeter provides our engineers with the deep insights needed to quickly identify root causes. Whether the problem lies within our CDN, with peering partners, or on the server side, we can pinpoint it with precision.By monitoring individual video requests, including headers and timings, and combining this data with our internal logging, we gain complete visibility and observability into our entire streaming pipeline. Surfmeter can also perform ping and traceroute tests from the same device, measuring video QoE, allowing our engineers to access all collected data through one API rather than manually connecting to devices for troubleshooting.Competitive benchmarking and future capabilitiesSurfmeter also enables us to benchmark our performance against other services and network providers. By deploying Surfmeter probes at customer-like locations, we can measure streaming from any source via different ISPs.This partnership reflects our commitment to transparency and data-driven service excellence. By leveraging AVEQ's Surfmeter solution, we ensure that our customers receive the best possible streaming performance, backed by objective, end-user-centric insights.Learn more about Gcore CDN

October 30, 2025 2 min read

Expert insights

How we engineered a single pipeline for LL-HLS and LL-DASH

Viewers in sports, gaming, and interactive events expect real-time, low-latency streaming experiences. To deliver this, the industry has rallied around two powerful protocols: Low-Latency HLS (LL-HLS) and Low-Latency DASH (LL-DASH).While they share a goal, their methods are fundamentally different. LL-HLS delivers video in a sequence of tiny, discrete files. LL-DASH delivers it as a continuous, chunked download of a larger file. This isn't just a minor difference in syntax; it implies completely different behaviors for the packager, the CDN, and the player.This duality presents a major architectural challenge: How do you build a single, efficient, and cost-effective pipeline that can serve both protocols simultaneously from one source?At Gcore, we took on this unification problem. The result is a robust, single-source pipeline that delivers streams with a glass-to-glass latency of approximately 2.0 seconds for LL-DASH and 3.0 seconds for LL-HLS. This is the story of how we designed it.Understanding the dualityTo build a unified system, we first had to deeply understand the differences in how each protocol operates at the delivery level.LL-DASH: the continuous feedMPEG-DASH has always been flexible, using a single manifest file to define media segments by their timing. Low-Latency DASH builds on this by using Chunked CMAF segments.Imagine a file that is still being written to on the server. Instead of waiting for the whole file to be finished, the player can request it, and the server can send it piece by piece using Chunked Transfer Encoding. The player receives a continuous stream of bytes and can start playback as soon as it has enough data.Single, long-lived files: A segment might be 2–6 seconds long, but it’s delivered as it’s being generated.Timing-based requests: The player knows when a segment should be available and requests it. The server uses chunked transfer to send what it has so far.Player-driven latency: The manifest contains a targetLatency attribute, giving the player a strong hint about how close to the live edge it should play.LL-HLS: the rapid-fire deliveryLL-HLS takes a different approach. It extends the traditional playlist-based HLS by breaking segments into even smaller chunks called Parts.Think of it like getting breaking news updates. The server pre-announces upcoming Parts in the manifest before they are fully available. The player then requests a Part, but the server holds that request open until the Part is ready to be delivered at full speed. This is called a Blocking Playlist Reload.Many tiny files (Parts): A 2-second segment might be broken into four 0.5-second Parts, each requested individually.Manifest-driven updates: The server constantly updates the manifest with new Parts, and uses special tags like #EXT-X-PART-INF and #EXT-X-SERVER-CONTROL to manage delivery.Server-enforced timing: The server controls when the player receives data by holding onto requests, which helps synchronize all viewers.A simplified diagram visually comparing the LL-HLS delivery of many small parts versus the LL-DASH chunked transfer of a single, larger segment over the same time period.These two philosophies demand different things from a CDN. LL-DASH requires the CDN to intelligently cache and serve partially complete files. LL-HLS requires the CDN to handle a massive volume of short, bursty requests and hold connections open for manifest updates. A traditional CDN is optimized for neither.Forging a unified strategyWith two different delivery models, where do you start? You find the one thing they both depend on: the keyframe.Playback can only start from a keyframe (or I-frame). Therefore, the placement of keyframes, which defines the Group of Pictures (GOP), is the foundational layer that both protocols must respect. By enforcing a consistent keyframe interval on the source stream, we could create a predictable media timeline. This timeline can then be described in two different “languages” in the manifests for LL-HLS and LL-DASH.A single timeline with consistent GOPs being packaged for both protocols.This realization led us to a baseline configuration, but each parameter involved a critical engineering trade-off:GOP: 1 second. We chose a frequent, 1-second GOP. The primary benefit is extremely fast stream acquisition; a player never has to wait more than a second for a keyframe to begin playback. The trade-off is a higher bitrate. A 1-second GOP can increase bitrate by 10–15% compared to a more standard 2-second GOP because you're storing more full-frame data. For real-time, interactive use cases, we prioritized startup speed over bitrate savings.Segment Size: 2 seconds. A 2-second segment duration provides a sweet spot. For LL-DASH and modern HLS players, it's short enough to keep manifest sizes manageable. For older, standard HLS clients, it prevents them from falling too far behind the live edge, keeping latency reduced even on legacy players.Part Size: 0.5 seconds. For LL-HLS, this means we deliver four updates per segment. This frequency is aggressive enough to achieve sub-3-second latency while being coarse enough to avoid overwhelming networks with excessive request overhead, which can happen with part durations in the 100–200ms range.Cascading challenges through the pipeline1. Ingest: predictability is paramountTo produce a clean, synchronized output, you need a clean, predictable input. We found that the encoder settings of the source stream are critical. An unstable source with a variable bitrate or erratic keyframe placement will wreck any attempt at low-latency delivery.For our users, we recommend settings that prioritize speed and predictability over compression efficiency:Rate control: Constant Bitrate (CBR)Keyframe interval: A fixed interval (e.g., every 30 frames for 30 FPS, to match our 1s GOP).Encoder tune: zerolatencyAdvanced options: Disable B-frames (bframes=0) and scene-cut detection (scenecut=0) to ensure keyframes are placed exactly where you command them to be.Here is an example ffmpeg command in Bash that encapsulates these principles:ffmpeg -re -i "source.mp4" -c:a aac -c:v libx264 \ -profile:v baseline -tune zerolatency -preset veryfast \ -x264opts "bframes=0:scenecut=0:keyint=30" \ -f flv "rtmp://your-ingest-url"2. Transcoding and packagingOur transcoding and Just-In-Time Packaging (JITP) layer is where the unification truly happens. This component does more than just convert codecs; it has to operate on a stream that is fundamentally incomplete.The primary challenge is that the packager must generate manifests and parts from media files that are still being written by the transcoder. This requires a tightly-coupled architecture where the packager can safely read from the transcoder's buffer.To handle the unpredictable nature of live sources, especially user-generated content via WebRTC, we use a hybrid workflow:GPU Workers (Nvidia/Intel): These handle the heavy lifting of decoding and encoding. Offloading to GPU hardware is crucial for minimizing processing latency and preserving advanced color formats like HDR+.Software Workers and Filters: These provide flexibility. When a live stream from a mobile device suddenly changes resolution or its framerate drops due to a poor connection, a rigid hardware pipeline would crash. Our software layer can handle these context changes gracefully, for instance, by scaling the erratic source and overlaying it on a stable, black-background canvas, meaning the output stream never stops.This makes our JITP a universal packager, creating three synchronized content types from a single, resilient source:LL-DASH (CMAF)LL-HLS (CMAF)Standard HLS (MPEG-TS) for backward compatibility3. CDN delivery: solving two problems at onceThis was the most intensive part of the engineering effort. Our CDN had to be taught how to excel at two completely different, high-performance tasks simultaneously.For LL-DASH, we developed a custom caching module we call chunked-proxy. When the first request for a new .m4s segment arrives, our edge server requests it from the origin. As bytes flow in from the origin, the chunked-proxy immediately forwards them to the client. When a second client requests the same file, our edge server serves all the bytes it has already cached and then appends the new bytes to both clients' streams simultaneously. It’s a multi-client cache for in-flight data.For LL-HLS, the challenges were different:Handling Blocked Requests: Our edge servers needed to be optimized to hold thousands of manifest requests open for hundreds of milliseconds without consuming excessive resources.Intelligent Caching: We needed to correctly handle cache statuses (MISS, EXPIRED) for manifests to ensure only one request goes to the origin per update, preventing a "thundering herd" problem.High Request Volume: LL-HLS generates a storm of requests for tiny part-files. Our infrastructure was scaled and optimized to serve these small files with minimal overhead.The payoff: ultimate flexibility for developersThis engineering effort wasn't just an academic exercise. It provides tangible benefits to developers building with our platform. The primary benefit is simplicity through unification, but the most powerful benefit is the ability to optimize for every platform.Consider the complex landscape of Apple devices. With our unified pipeline, you can create a player logic that does this:On iOS 17.1+: Use LL-DASH with the new Managed Media Source (MMS) API for ~2.0 second latency.On iOS 14.0 - 17.0: Use native LL-HLS for ~3.0 second latency.On older iOS versions: Automatically fall back to standard HLS with a reduced latency of ~9 seconds.This lets you provide the best possible experience on every device, all from a single backend and a single live source, without any extra configuration.Don't fly blind: observability in a low-latency worldA complex system is useless without visibility, and traditional metrics can be misleading for low-latency streaming. Simply looking at response_time from a CDN log is not enough.We had to rethink what to measure. For example:For an LL-HLS manifest, a high response_time (e.g., 500ms) is expected behavior, as it reflects the server correctly holding the request while waiting for the next part. A low response_time could actually indicate a problem. We monitor “Manifest Hold Time” to ensure this blocking mechanism is working as intended.For LL-DASH, a player requesting a chunk that isn't ready yet might receive a 404 Not Found error. While occasional 404s are normal, a spike can indicate origin-to-edge latency issues. This metric, combined with monitoring player liveCatchup behavior, gives a true picture of stream health.Gcore: one pipeline to serve them allThe paths of LL-HLS and LL-DASH may be different, but their destination is the same: real-time interaction with a global audience. By starting with a common foundation—the keyframe—and custom-engineering every component of our pipeline to handle this duality, we successfully solved the unification problem.The result is a single, robust system that gives developers the power of both protocols without the complexity of running two separate infrastructures. It’s how we deliver ±2.0s latency with LL-DASH and ±3.0s with LL-HLS, and it’s the foundation upon which we’ll build to push the boundaries of real-time streaming even further.

October 20, 2025 6 min read

News

Gcore successfully stops 6 Tbps DDoS attack

Gcore recently detected and mitigated one of the most powerful distributed denial-of-service (DDoS) attacks of the year, peaking at 6 Tbps and 5.3 billion packets per second (Bpps).This surge, linked to the AISURU botnet, reflects a growing trend of large-scale attacks. It reminds us how crucial effective protection has become for companies that depend on high availability and low latency. 6 Tbps 5.3 BppsThe attack in numbersPeak traffic: 6 TbpsPacket rate: 5.3 BppsMain protocol: UDP, typical of volumetric floods designed to overwhelm bandwidthGeographic concentration: 51% of sources originated in Brazil and 23.7% in the US, together accounting for nearly 75% of all trafficGeographic sources This regional concentration shows how today’s botnets are expanding across areas with high device connectivity and weaker security measures, creating an ideal environment for mass exploitation.How to strengthen your defensesThe 6 Tbps attack is not an isolated incident. It marks an escalation in DDoS activity across industries where performance and availability are critical to customer satisfaction and company revenue. To protect your business from large-scale DDoS attacks, consider the following key strategies:Adopt an adaptive DDoS protection that detects and mitigates attacks automatically.Leverage edge infrastructure to absorb malicious traffic closer to its source.Prepare for high traffic volumes by upgrading your infrastructure or partnering with a reliable DDoS protection provider that has the global capacity and resources to keep your services online during large-scale attacks.Keeping your business safe with GcoreTo stay ahead of these evolving threats, companies need solutions that deliver real-time detection, intelligent mitigation, and global reach. Gcore’s DDoS Protection was built to do precisely that, leveraging AI-driven traffic analysis and worldwide network capacity to block attacks before they impact your users.As attacks grow larger and more complex, staying resilient means being prepared. With the right protection in place, your customers will never know an attack happened in the first place.Learn more about 2025 cyberattack trends

October 14, 2025 2 min read

News

Gcore CDN updates: Dedicated IP and BYOIP now available

We’re pleased to announce two new premium features for Gcore CDN: Dedicated IP and Bring Your Own IP (BYOIP). These capabilities give customers more control over their CDN configuration, helping you meet strict security, compliance, and branding requirements.Many organizations, especially in finance and other regulated sectors, require full control over their network identity. With these new features, Gcore enables customers to use unique, dedicated IP addresses to meet compliance or security standards; retain ownership and visibility over IP reputation and routing, and deliver content globally while maintaining trusted, verifiable IP associations.Read on for more information about the benefits of both updates.Dedicated IP: exclusive addresses for your CDN resourcesThe Dedicated IP feature enables customers to assign a private IP address to their CDN configuration, rather than using shared ones. This is ideal for:Businesses that are subject to strict security or legal frameworks.Customers who want isolated IP resources to ensure consistent access and reputation.Teams using WAAP or other advanced security solutions where dedicated IPs simplify policy management.BYOIP: bring your own IP range to Gcore CDNWith Bring Your Own IP (BYOIP), customers can use their own public IP address range while leveraging the performance and global reach of Gcore CDN. This option is especially useful for:Resellers who prefer to keep Gcore infrastructure invisible to end clients.Enterprises maintaining brand consistency and control over IP reputation.How to get startedBoth features are currently available as paid add-ons and are configured manually by the Gcore team. To request activation or learn more, please contact Gcore Support or your account manager.We’re working on making these features easier to manage and automate in future releases. As always, we welcome your feedback on both the feature functionality and the request process—your insights help us improve the Gcore CDN experience for everyone.Get in touch for more information

October 13, 2025 1 min read

News

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

October 7, 2025 3 min read

Expert insights

Gcore Radar Q1–Q2 2025: three insights into evolving attack trends

Cyberattacks are becoming more frequent, larger in scale, and more sophisticated in execution. For businesses across industries, this means protecting digital resources is more important than ever. Staying ahead of attackers requires not only robust defense solutions but also a clear understanding of how attack patterns are changing.The latest edition of the Gcore Radar report, covering the first half of 2025, highlights important shifts in attack volumes, industry targets, and attacker strategies. Together, these findings show how the DDoS landscape is evolving, and why adaptive defense has never been more important.Here are three key insights from the report, which you can download in full here.#1. DDoS attack volumes continue to riseIn Q1–Q2 2025, the total number of DDoS attacks grew by 21% compared to H2 2024 and 41% year-on-year.The largest single attack peaked at 2.2 Tbps, surpassing the previous record of 2 Tbps in late 2024.The growth is driven by several factors, including the increasing availability of DDoS-for-hire services, the rise of insecure IoT devices feeding into botnets, and heightened geopolitical and economic tensions worldwide. Together, these factors make attacks not only more common but also harder to mitigate.#2. Technology overtakes gaming as the top targetThe distribution of attacks by industry has shifted significantly. Technology now represents 30% of all attacks, overtaking gaming, which dropped from 34% in H2 2024 to 19% in H1 2025. Financial services remain a prime target, accounting for 21% of attacks.This trend reflects attackers’ growing focus on industries with broader downstream impact. Hosting providers, SaaS platforms, and payment systems are attractive targets because a single disruption can affect entire ecosystems of dependent businesses.#3. Attacks are getting smarter and more complexAttackers are increasingly blending high-volume assaults with application-layer exploits aimed at web apps and APIs. These multi-layered tactics target customer-facing systems such as inventory platforms, payment flows, and authentication processes.At the same time, attack durations are shifting. While maximum duration has shortened from five hours to three, mid-range attacks lasting 10–30 minutes have nearly quadrupled. This suggests attackers are testing new strategies designed to bypass automated defenses and maximize disruption.How Gcore helps businesses stay protectedAs attack methods evolve, businesses need equally advanced protection. Gcore DDoS Protection offers over 200 Tbps filtering capacity across 210+ points of presence worldwide, neutralizing threats in real time. Integrated Web Application and API Protection (WAAP) extends defense beyond network perimeters, protecting against sophisticated application-layer and business-logic attacks. To explore the report’s full findings, download the complete Gcore Radar report here.Download Gcore Radar Q1-Q2 2025

September 23, 2025 2 min read

Expert insights

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

September 11, 2025 2 min read

Industry trends

Smart caching and predictive streaming: the next generation of content delivery

As streaming demand surges worldwide, providers face mounting pressure to deliver high-quality video without buffering, lag, or quality dips, no matter where the viewer is or what device they're using. That pressure is only growing as audiences consume content across mobile, desktop, smart TVs, and edge-connected devices.Traditional content delivery networks (CDNs) were built to handle scale, but not prediction. They reacted to demand, but couldn’t anticipate it. That’s changing.Today, predictive streaming and AI-powered smart caching are enabling a proactive, intelligent approach to content delivery. These technologies go beyond delivering content by forecasting what users will need and making sure it's there before it's even requested. For network engineers, platform teams, and content providers, this marks a major evolution in performance, reliability, and cost control.What are predictive streaming and smart caching?Predictive streaming is a technology that uses AI to anticipate what a viewer will watch next, so the content can be ready before it's requested. That might mean preloading the next episode in a series, caching popular highlights from a live event, or delivering region-specific content based on localized viewing trends.Smart caching supports this by storing that predicted content on servers closer to the viewer, reducing delays and buffering. Together, they make streaming faster and smoother by preparing content in advance based on user behavior.Unlike traditional caching, which relies on static popularity metrics or simple geolocation, predictive streaming is dynamic. It adapts in real time to what’s happening on the platform: user actions, traffic spikes, network conditions, and content trends. This results in:Faster playback with minimal bufferingReduced bandwidth and server loadHigher quality of experience (QoE) scores across user segmentsFor example, during the 2024 UEFA European Championship, several broadcasters used predictive caching to preload high-traffic game segments and highlight reels based on past viewer drop-off points. This allowed for instant replay delivery in multiple languages without overloading central servers.Why predictive streaming matters for viewersGlobally, viewers tend to binge-watch new streaming platform releases. For example, sci-fi-action drama Fallout got 25% of its annual US viewing minutes (2.9 billion minutes) in its first few days of release. The South Korean series Queen of Tears became Netflix's most-watched Korean drama of all time in 2024, amassing over 682.6 million hours viewed globally, with more than half of those watch hours occurring during its six-week broadcast run.A predictive caching system can take advantage of this launch-day momentum by pre-positioning likely-to-be-watched episodes, trailers, or bonus content at the edge, customized by region, device, or time of day.The result is a seamless, high-performance experience that anticipates user behavior and scales intelligently to meet it.Benefits for streaming providersTraditional CDNs often waste resources caching content that may never be viewed. Predictive caching focuses only on content that is likely to be accessed, leading to:Lower egress costsReduced server loadMore efficient cache hit ratiosOne of the core benefits of predictive streaming is latency reduction. By caching content at the edge before it’s requested, platforms avoid the delay caused by round-trips to origin servers. This is especially critical for:Live sports and eventsInteractive or real-time formats (e.g., polls, chats, synchronized streams)Edge environments with unreliable last-mile connectivityFor instance, during the 2024 Copa América, mobile viewers in remote areas of Argentina were able to stream matches without delay thanks to proactive edge caching based on geo-temporal viewing predictions.How it worksAt the core of predictive streaming is smart caching: the process of storing data closer to the end user before it’s explicitly requested. Here’s how it works:Data ingestion: The system gathers data on user behavior, device types, content popularity, and location-based trends.Behavior modeling: AI models identify patterns (e.g., binge-watching behaviors, peak-hour traffic, or regional content spikes).Pre-positioning: Based on predictions, the system caches video segments, trailers, or interactive assets to edge servers closest to where demand is expected.Real-time adaptation: As user behavior changes, the system continuously updates its caching strategy.Use cases across streaming ecosystemsSmart caching and predictive delivery benefit nearly every vertical of streaming.Esports and gaming platforms: Live tournaments generate unpredictable traffic surges, especially when underdog teams advance. Predictive caching helps preload high-interest match content, post-game analysis, and multilingual commentary before traffic spikes hit. This helps provide global availability with minimal delay.Corporate webcasts and investor events: Virtual AGMs or earnings calls need to stream seamlessly to thousands of stakeholders, often under compliance pressure. Predictive systems can cache frequently accessed segments, like executive speeches or financial summaries, at regional nodes.Education platforms: In EdTech environments, predictive delivery ensures that recorded lectures, supplemental materials, and quizzes are ready for users based on their course progression. This reduces lag for remote learners on mobile connections.VOD platforms with regional licensing: Content availability differs across geographies. Predictive caching allows platforms to cache licensed material efficiently and avoid serving geo-blocked content by mistake, while also meeting local performance expectations.Government or emergency broadcasts: During public health updates or crisis communications, predictive streaming can support multi-language delivery, instant replay, and mobile-first optimization without overloading networks during peak alerts.Looking forward: Personalization and platform governanceWe predict that the next wave of predictive streaming will likely include innovations that help platforms scale faster while protecting performance and compliance:Viewer-personalized caching, where individual user profiles guide what’s cached locally (e.g., continuing series, genre preferences)Programmatic cache governance, giving DevOps and marketing teams finer control over how and when content is distributedCross-platform intelligence, allowing syndicated content across services to benefit from shared predictions and joint caching strategiesGcore’s role in the predictive futureAt Gcore, we’re building AI-powered delivery infrastructure that makes the future of streaming a practical reality. Our smart caching, real-time analytics, and global edge network work together to help reduce latency and cost, optimize resource usage, and improve user retention and stream stability.If you’re ready to unlock the next level of content delivery, Gcore’s team is here to help you assess your current setup and plan your predictive evolution.Discover how Gcore streaming technologies helped fan.at boost subscription revenue by 133%

September 3, 2025 4 min read

Expert insights

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

August 25, 2025 2 min read

Industry trends

No capacity = no defense: rethinking DDoS resilience at scale

DDoS attacks are growing so massive they are overwhelming the very infrastructure designed to stop them. Earlier this year, a peak attack exceeding 7 Tbps was recorded, while 1–2 Tbps attacks have become everyday occurrences. Such volumes were unimaginable just a few years ago.Yet many businesses still depend on mitigation systems that were not designed to scale alongside this rapid attack growth. While these systems may have smart detection, that advantage is moot if physical infrastructure cannot handle the load. Today, raw capacity is non-negotiable — intelligent filtering alone isn’t enough; you need vast, globally distributed throughput.Lukasz Karwacki, Gcore’s Security Solution Architect specializing in DDoS, explains why modern DDoS protection requires immense capacity, global distribution, and resilient routing. Scroll down to watch him describe why a globally distributed defense model is now the minimum standard for mitigating devastating DDoS attacks.DDoS is a capacity war, not just a traffic spikeThe central challenge in DDoS mitigation today is the total attack volume versus total available throughput.Attacks do not originate from a single location. Global botnets harness compromised devices across Asia, Africa, Europe, and the Americas. When all this traffic converges on a single data center, it creates a structural mismatch: a single site’s limited capacity pitted against the full bandwidth of the internet.Anycast is non-negotiable for global capacityTo counter today’s attack volumes, mitigation capacity must be distributed globally, and that’s where Anycast routing plays a critical role.Anycast routes incoming traffic to the nearest available scrubbing center. If one region is overwhelmed or offline, traffic is automatically redirected elsewhere. This eliminates single points of failure and enables the absorption of massive attacks without compromising service availability.By contrast, static mitigation pipelines create bottlenecks: all traffic funnels through a single point, making it easy for attackers to overwhelm that location. Centralized mitigation means centralized failure. The more distributed your infrastructure, the harder it is to take down — that’s resilient network design.Why always-on cloud defense outperforms on-demand protectionSome DDoS defenses activate only when an attack is detected. These on-demand models may save costs but introduce a brief delay while traffic is rerouted and protections come online.Even a few seconds of delay can allow a high-speed attack to inflict damage.Gcore’s cloud-native DDoS protection is always-on, continuously monitoring, filtering, and balancing traffic across all scrubbing centers. This means no activation lag and no dependency on manual triggers.Capacity is the new baseline for protectionModern DDoS attacks focus less on sophistication and more on sheer scale. Attackers simply overwhelm infrastructure by flooding it with more traffic than it can handle.True DDoS protection begins with capacity planning — not just signatures or rulesets. You need sufficient bandwidth, processing power, and geographic distribution to absorb attacks before they reach your core systems.At Gcore, we’ve built a globally distributed DDoS mitigation network with over 200 Tbps capacity, 40+ protected data centers, and thousands of peering partners. Using Anycast routing and always-on defense, our infrastructure withstands attacks that other systems simply can’t.Many customers turn to Gcore for DDoS protection after other providers fail to keep up with attack capacity.Find out why Fawkes Games turned to Gcore for DDoS protection

August 22, 2025 2 min read

Industry trends

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

August 18, 2025 4 min read

News

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

August 7, 2025 2 min read

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