Setting up and running live streams can feel overwhelming for both newcomers and experienced creators due to technical complexities, the need for engaging content, the pressure of real-time performance, and the competition for viewer attention in a rapidly growing digital landscape. This guide will enable you to harness the benefits of effective live streaming with insights on selecting the right platform, configuring technical elements, and crafting engaging content.
What Is Live Streaming?
Live streaming involves broadcasting video of events over the internet as they are happening. It allows viewers to watch a video file as it’s being transmitted rather than waiting for the entire file to download before starting to watch. Unlike pre-recorded videos, which are edited and finalized before being shared, live streaming offers an immediate, authentic connection that unfolds in real time.
Live streaming is important because it gives audiences the opportunity to participate actively and engage with the video content, for example with TV shows, gaming sessions, or social media content. Through real-time interactions such as comments, likes, and live polls, audiences shift from passive spectators to involved community members.
Videos can be live-streamed using either on-premises solutions, which are managed on your own physical servers, or using cloud-based solutions hosted and maintained by an external provider.
How to Live Stream in 8 Simple Steps
Here’s a step-by-step guide that will ensure you’re well-prepared to stream engaging live content for your audience.
Step 1: Choose Your Streaming Solution
The first step is perhaps the most difficult, since you have so many options to consider and marketing decisions to make. When choosing a streaming platform, it’s crucial to understand both your target audience and technical requirements. Know where your audience stands in your marketing funnel, the content they interact with, and the platforms they use. Technical decisions like video quality or interface usability should align with audience needs. For instance, if your viewers prioritize high-quality visuals, a 4K-supporting platform becomes essential.
For a more comprehensive strategy, look at your competitors’ technical choices. This includes the platforms they use, video formats, resolutions, and monetization methods like pay-per-view or subscriptions. Conduct a SWOT analysis to align your own technical parameters and audience insights, ensuring you choose a platform that offers the best mix of reach, quality, and security.
Platforms to Consider
Certain audiences and goals may push you in the direction of a specific platform. Here are some popular choices sorted by industry.
- Mainstream platforms like YouTube Live, Facebook Live, and Twitter Live offer high visibility and support diverse video types.
- For real-time gaming streams with minimal delay, platforms such as Twitch and YouTube Gaming are ideal, offering features like custom overlays and high frame rates.
- Mixcloud, Picarto, and Behance cater to artists with lossless audio and high-resolution streaming.
- LinkedIn Live provides encrypted connections for professionals.
- E-commerce streamers can benefit from Amazon Live and Instagram Live Shopping, which feature secure payment gateways and advanced inventory management.
- For a more curated experience, personal websites with embedded video players offer customization and adaptive streaming.
- Multistreaming allows broadcasting to multiple platforms simultaneously without straining your internet connection. Understanding these technical specifications ensures you select the platform that aligns best with your technical and audience needs for effective engagement.
Features to Consider
Consider which features will enhance the overall streaming experience, such as:
- A video content management system
- Support for various revenue generation avenues
- Auto-record to VOD, automatically saving live streams for on-demand viewing later, while retaining monetization opportunities
- Global payment processing
- Go-live notifications, alerts that notify your audience when you start streaming
- Direct streaming from your branded domain rather than on social media, for more control over branding and audience data.
- Pre-registration pages and live event countdown features
- Integrated live chat and interaction features
- A landing page builder
- Live streaming to web and branded OTT apps (over-the-top or direct delivery media services); the ability to broadcast directly to your website and your own specialized media apps.
- Video editing and embedding tools
- Advanced live video analytics
Your Business Goals
When it comes to platform selection, ensure that your choice resonates with your business’ financial goals.
| Model | Description | Example |
|---|---|---|
| Subscription-based | Businesses generate revenue through the monthly or yearly subscription fees viewers pay to access a set of videos. | Netflix Hulu Disney+ Amazon Prime |
| Transaction-based or pay-per-view | Businesses generate revenue through the rental or purchase fee viewers pay for a particular video they want to watch. | YouTube Google TV Apple TV |
| Advertising-based | Businesses generate revenue through showing ads to viewers when watching video. | YouTube Facebook Watch Twitch |
Step 2: Gather Essential Components
Building a solid streaming setup involves several key components, and your choice of each will depend on factors like budget and quality requirements.
Cameras and Microphones
Webcams and smartphones might suffice for those new to content creation or operating on a low budget, but advanced professionals and larger organizations will require more specialized cameras and mics. For your audio setup, the microphone signal can either be routed through your camera for a simplified audio-video setup or managed via an external audio interface for better control over sound quality and levels.
Capture Cards
Capture cards help capture the signals from your audio and video sources. They become especially critical when using cameras with HDMI or SDI outputs, which are types of connections that deliver high-quality video but aren’t directly compatible with most computers. A capture card bridges this gap, allowing the high-quality signals to be processed by your computer. For those using software encoders, the card facilitates the essential transfer of the video signal from the camera to the computer for encoding. By contrast, USB cameras and microphones are designed for straightforward computer connectivity, negating the need for a separate capture card.
Lighting
Paying attention to lighting eliminates shadows and highlights facial features, enhancing the overall production value. Content creators can determine the most suitable lighting setup by considering their specific environment, desired mood, and the level of control they need over the lighting conditions, and testing the environment on camera before going live.
A Strong Internet Connection
An uninterrupted and robust internet connection is essential for seamless content delivery. For optimal results, choose a wired connection rather than Wi-Fi. Test your upload speed using Gcore’s speed test and aim for a minimum of 1 Mbps.
Step 3: Create Your Live Channel
Next, create your live channel on your chosen live streaming platform from Step 1. It will host your content, convert your files into a compatible format, and stream them. Add a unique name and description to your channel, upload a thumbnail image, and set your channel’s privacy settings. There are a number of other settings you can configure, such as your channel’s language, timezone, and monetization settings.
Step 4: Configure Your Video
Configuring your video settings according to the following best practices ensures that your live stream looks professional and engaging, providing your audience with an optimal viewing experience:
- Resolution: Start by setting your video resolution to 1920×1080 pixels, commonly referred to as 1080p HD. This resolution strikes a balance between video quality and bandwidth usage. It ensures that your content appears crisp and clear while also being accessible to viewers with varying internet speeds.
- Frame rate: Choose a frame rate of 30 frames per second (fps.) This standard frame rate provides a smooth and fluid motion for your live stream. It prevents choppiness and maintains the natural flow of movements, making your content more enjoyable to watch.
- Bitrate: Set your bitrate to a range between 4.5 to 6 Mbps. Your bitrate determines the amount of data transmitted per second in your live stream. Your goal is to find a balance between video quality and bandwidth availability. Adjust the bitrate based on your available upload speed and desired video quality. Higher bit rates result in better video quality but require more bandwidth.
Step 5: Set Up Your Encoder
To get your audio and video ready for online streaming, you’ll need an encoder. This tool translates the incoming signals into a digital format that your chosen streaming platform can understand. You have two main options: software encoders, which rely on your computer’s resources, or hardware encoders, dedicated devices specifically for this task. Many platforms offer a “preview” feature that allows you to ensure that your encoder is correctly configured and see how your stream will appear to viewers, before going live.
Software Encoders
If you’re using a software encoder like OBS Studio, launch the software and access the settings related to video and audio. Configure the resolution, frame rate, and audio settings to match the specifications you set in Step 4.
To connect your software encoder to your streaming platform, log in to your chosen live streaming platform (the one you set up in Step 3) and locate your stream key and stream URL. Enter these credentials into your encoder to establish a connection between your encoder and the streaming platform.
Hardware Encoder
For a hardware encoder, the audio and video data are usually sent to the platform through ports like HDMI or SDI. Access its settings via its dedicated interface.
Step 6: Monetize Your Video
Monetizing your live video stream allows you to generate revenue from your content. Here’s how to set up video monetization to make the most of your streaming efforts, with the exact steps differing based on the streaming platform you use:
- Choose your monetization method: Select the monetization strategy that aligns with your goals. Pay-per-view charges viewers a fee to access your stream, subscriptions provide exclusive content for paying subscribers, and advertising generates revenue through ad placements during your stream.
- Set up paywall or subscription: If you’re opting for pay-per-view or subscription-based monetization, configure the paywall settings or subscription tiers on your streaming platform. Define the pricing structure, access duration, and any exclusive perks for paying viewers.
- Enable advertising: For ad-based monetization, determine the ad placement options available on your streaming platform. Some platforms may automatically insert ads at specific intervals, while others might require you to integrate ad providers like Google AdSense.
- Test the monetization setup: Before going live, conduct a test run of your chosen monetization method to ensure that payments are processed correctly, and access is granted or ads are displayed as intended. This helps you avoid any issues during the actual live stream.
- Promote your monetization strategy: Use social media, email newsletters, and your website to inform your viewers about upcoming paid content, subscriptions, or ad-supported streams.
- Monitor performance and adapt: Keep a close eye on viewer engagement, feedback, and revenue generation. Monitor which monetization methods are most effective and make adjustments as necessary, based on audience preferences and trends.
Step 7: Add the Video Player to Your Website
To integrate the video player into your website and enhance accessibility and engagement for your viewers, following these steps.
- Acquire embed code: From your chosen live streaming platform’s dashboard, locate the embed code for your live stream. This code contains the necessary information to display the video player on your website.
- Access your website’s HTML: Log in to your website’s content management system or access the HTML code of the webpage where you want to embed the video player.
- Insert the embed code: Within the HTML code, find the section where you want to place the video player. This could be within a specific page or post. Insert the acquired embed code at the desired location.
- Adjust player size and position: Customize the size and position of the video player within the HTML code. You can specify dimensions in pixels or percentages to fit your webpage’s design. Use CSS styles to further refine the appearance and alignment of the player.
- Save and publish changes: After inserting and adjusting the embed code, save your changes in the HTML editor. Preview the webpage to ensure the video player displays correctly.
- Test and troubleshoot: Load the webpage on different devices and browsers to test the responsiveness and compatibility of the embedded video player. If any issues arise, review your HTML code and CSS styles for errors and inconsistencies.
- Ensure mobile responsiveness: Use media queries in your CSS styles to adapt the player’s size and layout for various screen sizes.
- Integrate analytics: If your live streaming platform offers analytics, consider integrating tracking codes or scripts into the HTML to gather insights into viewer engagement, play duration, and other metrics.
Step 8: Start Sharing Your Live Stream
To maximize the impact of your live stream, announce the event well in advance. Send out reminder messages that include direct links to the stream. Create anticipation by sharing visually engaging teasers and use live features to reach a wider audience.
Start your live stream on time, actively interact with your viewers, and maintain engagement during the broadcast. Encourage viewers to share the stream and keep a recording for future distribution.
Live Streaming Best Practices
Live streaming offers a wealth of benefits for engaging audiences by bringing a sense of immediacy and authenticity to your content. To effectively make the most of these advantages, here are key practices that can elevate your live streaming experience:
- Define your niche and audience: Choose a specific content niche to establish a dedicated viewer base that resonates with your content.
- Craft a clear schedule to promote consistency: Determine the optimal times to go live based on your audience’s preferences. Consistency in your streaming schedule helps viewers anticipate your broadcasts.
- Enhance viewer interaction with engagement tools: Interactive features like live chat and polls convert passive viewers into active participants, fostering a sense of community.
- Prioritize video and sound quality: High production values contribute to a professional appearance and provide viewers with a satisfying viewing experience.
- Expand your reach through multistreaming: By broadcasting your content across different platforms like Twitter, Facebook, and Instagram, you can tap into diverse audiences and maximize your impact.
- Access real-time analytics for better insights: This information empowers you to adapt your content on the spot for optimal engagement.
- Archive live streams for later: Use a video content management system to store your live sessions so people can watch them after the event, adding longevity to your content and offering more touchpoints for audience engagement.
Gcore for Live Streaming: The All-in-One Solution
Gcore’s Streaming Platform is an all-in-one solution, providing low-latency live streaming to your viewers. With high-quality content delivery up to 4K/8K, a delay of no more than 4–5 seconds, and the ability to scale to 100+ million viewers, Gcore ensures that viewers can enjoy seamless live streams across the globe. The platform’s adaptability and the added convenience of monetization tools make it an effective solution for both content creation and business development.
Conclusion
Live streaming opens up a fantastic avenue to engage with your audience in a truly meaningful way. Now that you’ve mastered the technical aspects of selecting platforms and equipment, setting up live streams, and adopting best practices, you’re more than ready to start engaging with audiences, sharing expertise, connecting with like-minded individuals, and showcasing products in an unparalleled way.
Ready to get started? Discover the future of live streaming with Gcore and unlock endless opportunities for engaging and immersive content creation. Start creating live streams with Gcore.
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What are virtual machines?
A virtual machine (VM), also called a virtual instance, is a software-based version of a physical computer. Instead of running directly on hardware, a VM operates inside a program that emulates a complete computer system, including a processor, memory, storage, and network connections. This allows multiple VMs to run on a single physical machine, each with its own operating system and applications, as if they were independent computers.VMS are useful because they provide flexibility, isolation, and scalability. Since each VM is self-contained, it can run different operating systems (like Windows, Linux, or macOS) on the same hardware without affecting other VMs or the host machine. This makes them ideal for testing software, running legacy applications, or efficiently using server resources in data centers. Because VMs exist as software, they can be easily copied, moved, or backed up, making them a powerful tool for both individuals and businesses.Read on to learn about types of VMs, their benefits, common use cases, and how to choose the right VM provider for your needs.How do VMs work?A virtual machine (VM) runs inside a program called a hypervisor, which acts as an intermediary between the VM and the actual computer hardware. Every time a VM needs to perform an action—such as running software, accessing storage, or using the processor—the hypervisor intercepts these requests and decides how to allocate resources like CPU power, memory, and disk space. You can think of a hypervisor as an operating system for VMs, managing multiple virtual machines on a single physical computer. Popular hypervisors like VirtualBox and VMware enable users to run multiple operating systems simultaneously while providing strong isolation.Modern hypervisors optimize performance by giving VMs direct access to certain hardware components when possible, reducing the need for constant intervention. However, some level of overhead remains because the hypervisor still needs to manage and coordinate resources efficiently. This means that while VMs can leverage most of the system’s hardware, they can’t use 100% of it, as some processing power is always reserved for managing virtualization itself. This small trade-off is often worth it, as hypervisors keep each VM isolated and secure, preventing one VM from interfering with another.VM layersFigure 1 illustrates the layers of a system virtual machine setup. The layer model can vary depending on the hypervisor. Some hypervisors include a built-in host operating system, while modern hardware offers native virtualization support. Many hypervisors can also manage multiple physical machines and VMs efficiently.VM snapshots are an essential feature in cloud computing, allowing users to quickly restore a virtual machine to a previous state.Figure 1: Layers of system virtual machinesHypervisors that emulate hardware architectures different from what the guest OS expects have a bigger overhead, as they can’t relay commands directly to the hardware without first translating them.VM snapshotsVM snapshots are an essential feature in cloud computing, allowing users to quickly restore a virtual machine to a previous state. The hypervisor can save the complete state of the VM and restore it at a later time to skip the boot process of the guest OS. The hypervisor can also move these snapshots between different physical machines, making the software running in the VM completely independent from the underlying hardware.What are the benefits of using VMs?Virtual machines offer benefits including resource efficiency, isolation, simplified operations, easy migration, faster deployment, cost savings, and security. Let’s look at these one by one.Multiple VMs can run on a single physical machine, making sharing resources between various guest operating systems easier. This is especially important when each guest OS needs to be isolated from the others, such as when they belong to different customers of a cloud service provider. Sharing resources through VMs makes running a server cheaper because you don’t have to buy or rent a whole physical machine, but only parts of it.Since VMs abstract the underlying hardware, they also improve resilience. If the physical machine fails, the hypervisor can perform a quick recovery by moving the snapshots to another machine without changing the guest OS installations to minimize downtime. This abstraction also allows operations teams to focus their deployment efforts on a standardized VM instead of considering different physical implementations.Migrations become easier with snapshots as you can simply move them to a faster machine without modifying the software running inside the VM.Faster deployments are possible because starting a VM is just a software execution instead of setting up a physical server in a data center. While you had to buy a server or rent it for months, with fast deployments, you can now rent a machine for hours, minutes, or even seconds, which allows for quite some savings.Modern CPUs have built-in virtualization features that enable easy resource sharing and enforce the isolation at the hardware layer. This prevents the services of one VM from accessing the resources of the others, improving security compared to running multiple apps inside one OS.Common use cases for VMsVMs have a range of use cases. Let’s look at the most popular ones.Cloud computingThe most popular use case is cloud computing, where VMs allow the secure sharing of the cloud provider’s resources, enabling their customers to rent only the resources they need for the period their workload will run.Software development and testingSoftware development often requires specific tools and libraries that aren’t available on a production machine, so having a development VM with all these tools preinstalled can be helpful. An example is cloud IDEs, which look and feel like regular IDEs but run on a cloud VM. A developer can have one for each project with the required dev tools installed.VMs also allow a developer to set up a machine for software testing that looks exactly like the production environment. Here, the opposite of the development VM is required; it should not have any development tools installed because they would also be missing from production.Cross-platform developmentA special case of the software development use case is cross-platform development. When you implement an app for Android or iOS, for example, you usually don’t do this on a mobile device but on your computer. With VMs, developers can simulate different hardware environments, enabling cross-platform testing without requiring physical devices.Legacy system supportIf the hardware your application requires is no longer in production, a VM might be the only way to keep running your software without reimplementing it. This is similar to the cross-platform development use case, as the VM emulates different hardware, but the difference is that the hardware no longer exists.How to choose the right VM providerTo find the right provider for your workload, the most important factor to assess is your own workload requirements. Ask the following questions and compare the answers to what providers offer.Is your workload compute or I/O-bound?Many workloads, like web servers, are I/O-bound. They don’t make complex calculations but rather simply load data and send it over the network. If you need a VM for an I/O-bound workload, you care more about disk and memory size, as well as network speed.However, compute-heavy workloads, such as AI inference or Kubernetes clusters, require careful resource allocation. If you’re evaluating whether to run Kubernetes on bare metal or VMs, check out our white paper on Bare Metal vs. VM-based Kubernetes Clusters for an in-depth comparison.If your workload is compute-bound instead, you need a high-performance CPU or a GPU and loads of memory. An AI inference engine, for example, only sends a bit of text to a client, but it does many calculations to generate this text.How long will your workload run?Web servers usually run indefinitely, but some workloads only run a few hours or minutes. If you’re doing AI training, you don’t want to pay for your huge VM cluster 24/7 if it only runs a few hours or days a week. In such cases, looking for a provider that allows renting your desired VM type hourly on a pay-as-you-go model might be worthwhile.Certain cloud providers offer cost-effective spot instances, which provide lower prices for non-critical workloads that can tolerate interruptions. These cheap VMs can get shut down at any time with minimal notice, but if your calculations aren’t time-critical, you might save quite a bit of money here.How does your workload scale?Scaling in the cloud is usually done horizontally. That is, by adding more VMs and distributing the work between them. Workloads can have different requirements for when and how fast they must be added and removed.In the AI training example, you might know in advance that one training takes more resources than the other, so you can provision enough VMs when starting. However, a web server workload might change its requirements constantly. Hence, you need a load balancer that automatically scales the instances up and down depending on the number of clients that want to access your service.Do you handle sensitive data?You might have to comply with specific laws and regulations depending on your jurisdiction(s) and industry. This means you must check whether the cloud provider also complies. How secure are their data centers? Where are they located? Do they support encryption in transit, at rest, and in process?What are your reliability requirements?Reliability is a question of costs and, again, of compliance. You might get into financial or regulatory troubles if your workload can’t run. Cloud providers often boast about their guaranteed uptimes, but remember that 99% uptime a year still means over three days of potential downtime. Check your needs and then seek a provider that can meet them reliably.Do you need customer support?If your organization doesn’t have the know-how for operating VMs in the cloud, you might need technical support from the provider. Most cloud providers are self-service, offering you a GUI and an API to manage resources. If your business lacks the resources to operate VMs, seek out a provider that can manage VMs on your behalf.SummaryVMs are a core technology for cloud computing and software development alike. They enable efficient resource sharing, improve security with hardware-enforced guest isolation, and simplify migration and disaster recovery. Choosing the right VM provider starts with understanding your workload requirements, from resource allocation to security and scalability.Maximize cloud efficiency with Gcore Virtual Machines—engineered for high performance, seamless scalability, and enterprise-grade security at competitive pricing. Whether you need to run workloads at scale or deploy applications in seconds, our VMs provide enterprise-grade security, built-in resilience, and optimized resource allocation, all powered by cutting-edge infrastructure. With global reach, fast provisioning, egress traffic included, and pay-as-you-go pricing, you get the scalability and reliability your business needs without overspending. Start your journey with Gcore VMs today and experience cloud computing that’s built for speed, security, and savings.Discover Gcore VMs
Why do bad actors carry out Minecraft DDoS attacks?
One of the most played video games in the world, Minecraft, relies on servers that are frequently a target of distributed denial-of-service (DDoS) attacks. But why would malicious actors target Minecraft servers? In this article, we’ll look at why these servers are so prone to DDoS attacks and uncover the impact such attacks have on the gaming community and broader cybersecurity landscape. For a comprehensive analysis and expert tips, read our ultimate guide to preventing DDoS attacks on Minecraft servers.Disruption for financial gainFinancial exploitation is a typical motivator for DDoS attacks in Minecraft. Cybercriminals frequently demand ransom to stop their attacks. Server owners, especially those with lucrative private or public servers, may feel pressured to pay to restore normalcy. In some cases, bad actors intentionally disrupt competitors to draw players to their own servers, leveraging downtime for monetary advantage.Services that offer DDoS attacks for hire make these attacks more accessible and widespread. These malicious services target Minecraft servers because the game is so popular, making it an attractive and easy option for attackers.Player and server rivalriesRivalries within the Minecraft ecosystem often escalate to DDoS attacks, driven by competition among players, servers, hosts, and businesses. Players may target opponents during tournaments to disrupt their gaming experience, hoping to secure prize money for themselves. Similarly, players on one server may initiate attacks to draw members to their server and harm the reputation of other servers. Beyond individual players, server hosts also engage in DDoS attacks to disrupt and induce outages for their rivals, subsequently attempting to poach their customers. On a bigger scale, local pirate servers may target gaming service providers entering new markets to harm their brand and hold onto market share. These rivalries highlight the competitive and occasionally antagonistic character of the Minecraft community, where the stakes frequently surpass in-game achievements.Personal vendettas and retaliationPersonal conflicts can occasionally be the source of DDoS attacks in Minecraft. In these situations, servers are targeted in retribution by individual gamers or disgruntled former employees. These attacks are frequently the result of complaints about unsolved conflicts, bans, or disagreements over in-game behavior. Retaliation-driven DDoS events can cause significant disruption, although lower in scope than attacks with financial motivations.Displaying technical masterySome attackers carry out DDoS attacks to showcase their abilities. Minecraft is a perfect testing ground because of its large player base and community-driven server infrastructure. Successful strikes that demonstrate their skills enhance reputations within some underground communities. Instead of being a means to an end, the act itself becomes a badge of honor for those involved.HacktivismHacktivists—people who employ hacking as a form of protest—occasionally target Minecraft servers to further their political or social goals. These attacks are meant to raise awareness of a subject rather than be driven by personal grievances or material gain. To promote their message, they might, for instance, assault servers that are thought to support unfair policies or practices. This would be an example of digital activism. Even though they are less frequent, these instances highlight the various reasons why DDoS attacks occur.Data theftMinecraft servers often hold significant user data, including email addresses, usernames, and sometimes even payment information. Malicious actors sometimes launch DDoS attacks as a smokescreen to divert server administrators’ attention from their attempts to breach the server and steal confidential information. This dual-purpose approach disrupts gameplay and poses significant risks to user privacy and security, making data theft one of the more insidious motives behind such attacks.Securing the Minecraft ecosystemDDoS attacks against Minecraft are motivated by various factors, including personal grudges, data theft, and financial gain. Every attack reveals wider cybersecurity threats, interferes with gameplay, and damages community trust. Understanding these motivations can help server owners take informed steps to secure their servers, but often, investing in reliable DDoS protection is the simplest and most effective way to guarantee that Minecraft remains a safe and enjoyable experience for players worldwide. By addressing the root causes and improving server resilience, stakeholders can mitigate the impact of such attacks and protect the integrity of the game.Gcore offers robust, multi-layered security solutions designed to shield gaming communities from the ever-growing threat of DDoS attacks. Founded by gamers for gamers, Gcore understands the industry’s unique challenges. Our tools enable smooth gameplay and peace of mind for both server owners and players.Want an in-depth look at how to secure your Minecraft servers?Download our ultimate guide
How to deploy DeepSeek 70B with Ollama and a Web UI on Gcore Everywhere Inference
Large language models (LLMs) like DeepSeek 70B are revolutionizing industries by enabling more advanced and dynamic conversational AI solutions. Whether you’re looking to build intelligent customer support systems, enhance content generation, or create data-driven applications, deploying and interacting with LLMs has never been more accessible.In this tutorial, we’ll show you exactly how to set up DeepSeek 70B using Ollama and a Web UI on Gcore Everywhere Inference. By the end, you’ll have a fully functional environment where you can easily interact with your custom LLM via a user-friendly interface. This process involves three simple steps: deploying Ollama, deploying the web UI, and configuring the web UI and connecting to Ollama.Let’s get started!Step 1: Deploy OllamaLog in to Gcore Everywhere Inference and select Deploy Custom Model.In the model image field, enter ollama/ollama.Set the Port to 11434.Under Pod Configuration, configure the following:Select GPU-Optimized.Choose a GPU type, such as 1×A100 or 1×H100.Choose a region (e.g., Luxembourg-3).Set an autoscaling policy or use the default settings.Name your deployment (e.g., ollama).Click Deploy model on the right side of the screen.Once deployed, you’ll have an Ollama endpoint ready to serve your model.Step 2: Deploy the Web UI for OllamaGo back to the Gcore Everywhere Inference console and select Deploy Custom Model again.In the Model Image field, enter ghcr.io/open-webui/open-webui:main.Set the Port to 8080.Under Pod Configuration, set:CPU-Optimized.Choose 4 vCPU / 16 GiB RAM.Select the same region as before (e.g., Luxembourg-3).Configure an autoscaling policy or use the default settings.Name your deployment (e.g., webui).Click Deploy model on the right side of the screen.Once deployed, navigate to the Web UI endpoint from the Gcore Customer Portal.Step 3: Configure the Web UIFrom the Web UI endpoint and set up a username and password when prompted.Log in and navigate to the admin panel.Go to Settings → Connections → Disable the OpenAI API integration.In the Ollama API field, enter the endpoint for your Ollama deployment. You can find this in the Gcore Customer Portal. It will look similar to this: https://<your-ollama-deployment>.ai.gcore.dev/.Click Save to confirm your changes.Step 4: Pull and Use DeepSeek 70BOpen the chat section in the Web UI.In the Select a model field, type deepseek-r1:70b.Click Pull to download the model.Wait for the download to complete.Once downloaded, select the model and start chatting!Your AI environment is ready to exploreBy following these steps, you’ve successfully deployed DeepSeek 70B on Gcore Everywhere Inference with Ollama. This setup provides a powerful and user-friendly environment for experimenting with LLMs, prototyping AI-driven features, or integrating advanced conversational AI into your applications.Ready to unlock the full potential of AI? Gcore Everywhere Inference offers outstanding scalability, performance, and support, making it the perfect solution for developers and businesses working with advanced AI models. Dive deeper into our powerful tools and resources by exploring our AI blog and docs.Discover Gcore Everywhere Inference
How do CDNs work?
Picture this: A visitor lands on your website excited to watch a video, buy an item, or explore your content. If your page loads too slowly, they may leave before it even loads completely. Every second matters when it comes to customer retention, engagement, and purchasing patterns.This is where a content delivery network (CDN) comes in, operating in the background to help end users access digital content quickly, securely, and without interruption. In this article, we’ll explain how a CDN works to optimize the delivery of websites, applications, media, and other online content, even during high-traffic spikes and cyberattacks. If you’re new to CDNs, you might want to check out our introductory article first.Key components of a CDNA CDN is a network of interconnected servers that work together to optimize content delivery. These servers communicate to guarantee that data reaches users as quickly and efficiently as possible. The core of a CDN consists of globally distributed edge servers, also known as points of presence (PoPs):Origin server: The central server where website data is stored. Content is distributed from the origin to other servers in the CDN to improve availability and performance.Points of presence (PoPs): A globally distributed network of edge servers. PoPs store cached content—pre-saved copies of web pages, images, videos, and other assets. By serving cached content from the nearest PoP to the user, the CDN reduces the distance data needs to travel, improving load times and minimizing strain on the origin server. The more PoPs a network has, the faster content is served globally.How a CDN delivers contentCDNs rely on edge servers to store content in a cache, enabling faster delivery to end users. The delivery process differs depending on whether the content is already cached or needs to be fetched from the origin server.A cache hit occurs when the requested content is already stored on a CDN’s edge server. Here’s the process:User requests content: When a user visits a website, their device sends a request to load the necessary content.Closest edge server responds: The CDN routes the request to the nearest edge server to the user, minimizing travel time.Content delivered: The edge server delivers the cached content directly to the user. This is faster because:The distance between the user and the server is shorter.The edge server has already optimized the content for delivery.What happens during a cache miss?A cache miss occurs when the requested content is not yet stored on the edge server. In this case, the CDN fetches the content from the origin server and then updates its cache:User requests content: The process begins when a user’s device sends a request to load website content.The closest server responds: As usual, the CDN routes the request to the nearest edge server.Request to the origin server: If the content isn’t cached, the CDN fetches it from the origin server, which houses the original website data. The edge server then delivers it to the user.Content cached on edge servers: After retrieving the content, the edge server stores a copy in its cache. This ensures that future requests for the same content can be delivered quickly without returning to the origin server.Do you need a CDN?Behind every fast, reliable website is a series of split-second processes working to optimize content delivery. A CDN caches content closer to users, balances traffic across multiple servers, and intelligently routes requests to deliver smooth performance. This reduces latency, prevents downtime, and strengthens security—all critical for businesses serving global audiences.Whether you’re running an e-commerce platform, a streaming service, or a high-traffic website, a CDN ensures your content is delivered quickly, securely, and without interruption, no matter where your users are or how much demand your site experiences.Take your website’s performance to the next level with Gcore CDN. Powered by a global network of over 180+ points of presence, our CDN enables lightning-fast content delivery, robust security, and unparalleled reliability. Don’t let slow load times or security risks hold you back. Contact our team today to learn how Gcore can elevate your online presence.Discover Gcore CDN
How to get the size of a directory in Linux
Understanding how to check directory size in Linux is critical for managing storage space efficiently. Understanding this process is essential whether you’re assessing specific folder space or preventing storage issues.This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.What is a Linux directory?A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.#1 Get Linux directory size using the du commandUsing the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).Check the size of a specific directory in LinuxTo get the size of a specific directory, open your terminal and type the following command:du -sh /path/to/directoryIn this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.Check the size of all directories in LinuxTo get the size of all files and directories within the current directory, use the following command:sudo du -h /path/to/directoryExample: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.#2 Get Linux directory size using ncduIf you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.For Debian or Ubuntu, use this command:sudo apt-get install ncduOnce installed, run ncdu followed by the path to the directory you want to analyze:ncdu /path/to/directoryThis will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:#3 Get Linux directory size using 1s -1aYou can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:-l (long listing format)Displays the detailed information for each file and directoryShows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name-a (all files)Instructs ls to include all files, including hidden files and directoriesIncludes hidden files on Linux that typically have names beginning with a . (dot)ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.Example: When you enter ls -la command and press Enter, you will see an output similar to this:Each line includes:File type and permissions (e.g., drwxr-xr-x):The first character indicates the file type- for a regular filed for a directoryl for a symbolic linkThe next nine characters are permissions in groups of three (rwx):r = readw = writex = executePermissions are shown for three classes of users: owner, group, and others.Number of links (e.g., 2):For regular files, this usually indicates the number of hard linksFor directories, it often reflects subdirectory links (e.g., the . and .. entries)Owner and group (e.g., user group)File size (e.g., 4096 or 1045 bytes)Modification date and time (e.g., Jan 7 09:34)File name (e.g., .bashrc, notes.txt, Documents):Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)ConclusionThat’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:Affordable shared compute resources starting from €3.2 per monthDeploy across 50+ cloud regions with dedicated servers for low-latency applicationsSecure apps and data with DDoS protection, WAF, and encryption at no additional costGet started today
What is AI inference and how does it work?
Artificial intelligence (AI) inference is what happens when a trained AI model is used to predict outcomes from new, unseen data. While training focuses on learning from historical datasets, inference is about putting that learned knowledge into action—such as identifying production bottlenecks before they happen, converting speech to text, or guiding self-driving cars in real time. This article walks you through the basics of AI inference and shows how to get started.What is AI inference?AI inference is the application phase of artificial intelligence. Once a model has been trained on large datasets, it shifts from “learning mode” to “doing mode”—providing predictions or decisions from new data inputs.For example, an e-commerce platform with a model trained on purchasing behavior uses inference to personalize recommendations for each site visitor. Without re-training from scratch, the model quickly adapts to new browsing patterns and purchasing signals, offering instant, relevant suggestions.By enabling actionable insights, inference is transforming how businesses and technologies function, empowering relevance and instant responsiveness in an increasingly data-driven world.How does AI inference work? A practical guideAI inference has four steps: data preparation, model loading, processing and prediction, and output generation.#1 Data preparationThe first step involves transforming raw input—such as text, images, or numerical data—into a format that the AI model can process. For instance, customer feedback might be converted into numerical representations of words and patterns, or an image could be resized and normalized. Proper data preparation ensures that the AI model can effectively understand and analyze the input. For businesses, this means making sure that input data is clean, well-structured, and formatted according to the model’s requirements.#2 Model loadingOnce the input data is ready, the trained AI model is loaded into memory. This model, equipped with patterns and relationships learned during training, acts as the foundation for predictions and decisions.Businesses must make sure that their infrastructure is capable of quickly loading and deploying AI models, especially during high-demand periods. We simplify this process by providing a high-performance platform with global scalability. Your models are loaded and operational in seconds, whether you’re using a custom model or an open-source one.#3 Processing and predictionIn this step, the prepared data is passed through the model’s neural networks, which apply learned patterns to generate insights or predictions. For example, a customer service AI might analyze incoming messages to determine if they express satisfaction or frustration.The speed and accuracy of this stage depend on access to low-latency infrastructure capable of handling complex calculations. Our edge inference solution means data processing happens close to the source, reducing latency and enabling real-time decision making.#4 Output generationThe final stage translates the model’s mathematical outputs into meaningful insights, such as predictions, labels, or recommendations. These outputs must be integrated into business workflows or customer-facing applications in a way that’s easy to understand and actionable.We help streamline this step by offering APIs and integration tools that allow businesses to seamlessly incorporate inference results into their operations, so outputs are accessible and actionable in real time.A real-life exampleLet’s look at how this works in practice. Consider a retail business implementing AI for inventory management. The system continuously:Receives data from point-of-sale systems and warehouse scannersProcesses this information through trained AI modelsGenerates predictions about future inventory needsAdjusts order quantities and timing automaticallyAll of this happens in milliseconds, making real-time decisions possible. However, the speed and efficiency depend on choosing the right infrastructure for your needs.The technology stack behind inferenceTo make this process work smoothly, specialized computing infrastructure and software need to work together.Computing infrastructureModern AI inference relies on specialized hardware designed to process mathematical operations quickly. While training AI models often requires expensive, high-powered graphics processors (GPUs), inference can run on more cost-effective hardware options:CPUs: Suitable for smaller-scale applicationsEdge devices: For processing data locally on smartphones or IoT devices or other hardware closer to the data source, resulting in low latency and better privacy.Cloud-based inference servers: Designed for handling large-scale operations, enabling centralized processing and flexible scaling.When evaluating computing infrastructure for AI, businesses should prioritize solutions that address latency, scalability, and ease of use. Edge inference capabilities are essential for deploying models closer to end users, which optimizes performance globally even during peak demand. Flexible access to diverse hardware options like GPUs, CPUs, and advanced accelerators ensures adaptability, while user-friendly tools and automated scaling enable seamless management and consistent performance.Software optimizationThe efficiency of inference depends heavily on software optimization. When done right, software optimization ensures that AI applications are fast, responsive, and scalable, making them practical for real-world use.Look for the following to identify a solution that reduces inference processing time and supports optimized results:Model compression and optimization: The computational load is reduced and inference occurs faster—without sacrificing accuracy.Workload distribution and automation: This means that resources are allocated efficiently and cost-effectively.Integration: Look for APIs and tools that connect seamlessly with existing business systems.The future of AI inferenceWe anticipate three major trends for the future of AI inference.First, we’re seeing a dramatic shift toward specialized AI accelerators and custom silicon. New chips are being developed and existing ones optimized specifically for inference workloads. These purpose-built processors are delivering significant improvements in both performance and energy efficiency compared to traditional GPUs. This specialization is making AI inference more cost-effective and environmentally sustainable, particularly for companies running large-scale operations.The second major trend is the emergence of lightweight, efficient models designed specifically for inference. While large language models like GPT-4 showcase the potential of AI, many businesses are finding that smaller, task-specific models can deliver comparable or better results for their particular needs. These “small language models” (SLMs) and domain-adapted models are trained on focused datasets and optimized for specific tasks, making them more practical for real-world deployment. This approach is particularly valuable for edge computing scenarios where computing resources are limited.Finally, the infrastructure for AI inference is becoming more sophisticated and accessible. Advanced orchestration tools are automating the complex process of model deployment, scaling, and monitoring. These platforms can automatically optimize model performance based on factors like latency requirements, cost constraints, and traffic patterns. This automation is making it possible for companies to deploy AI solutions without maintaining large specialized teams of ML engineers.Dive into more of our predictions for AI inference in 2025 and beyond in our dedicated article.Accelerate inference adoption for your businessAI inference is rapidly becoming a differentiator for businesses. By applying trained AI models to new data, companies can make instant predictions, automate decision-making, and optimize operations across industries. However, achieving these benefits depends on having the right infrastructure and expertise behind the scenes. This is where the choice of inference provider plays a critical role. The provider’s infrastructure determines latency, scalability, and overall efficiency, which directly affect business outcomes. A well-equipped provider allows businesses to maximize the value of their AI investments.At Gcore, we are uniquely positioned to meet these needs with our edge inference solution. Leveraging a secure, global network of over 180 points of presence equipped with NVIDIA GPUs, we deliver ultra-fast, low-latency inference capabilities. Intuitively deploy and scale open-source or custom models on our powerful platform that accelerates AI adoption for a competitive edge in an increasingly AI-driven world.Get a complimentary consultation about your AI inference needs
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