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How to Secure Your Kubernetes Cluster

  • By Gcore
  • May 10, 2024
  • 8 min read
How to Secure Your Kubernetes Cluster

As Kubernetes continues to dominate as the go-to for producing containerized applications, securing these clusters becomes vital. This article discusses the complexities of Kubernetes security, highlighting common vulnerabilities and strategic defenses. We explore strategies to protect your Kubernetes environment, from understanding the four C’s of security—Cloud, Clusters, Containers, and Code—to learning the management of Kubernetes secrets. Learn how to strengthen your cluster and why these measures are crucial for resilient infrastructure.

What Is the Security Strategy of Kubernetes

To secure the integrity and confidentiality of data within the cluster, Kubernetes security takes a structured approach that focuses on Authentication, Authorization, and Admission Control. Below, we will look at these basic components of Kubernetes security and demonstrate the appropriate configurations and instructions to improve security measures.

#1 Authentication

Authentication is the initial stage in the security chain, guaranteeing that only legitimate users may access the Kubernetes API. Kubernetes supports a variety of authentication mechanisms:

  • Static Passwords and Tokens. This method relies on passwords and tokens that are predefined in the Kubernetes API server’s initial configuration. These credentials are often configured when the cluster is created, providing a basic level of access control. This strategy is commonly used for first installs or in contexts with reduced complexity and security requirements.
  • Service Account Tokens. Kubernetes manages customized tokens that are automatically assigned to pods. They are generally used to provide programmatic access to the Kubernetes API from within the pod. Service account tokens enable secure service-to-service communication within the cluster, ensuring that applications have the necessary permissions to run properly even without human interaction.
  • Client Certificates. For a more secure method of authentication, Kubernetes supports the use of X509 certificates. This approach uses public key infrastructure (PKI) to verify the identity of users and systems. Client certificates must be signed by a trusted certificate authority (CA) that the Kubernetes API server recognizes. This method is highly secure and commonly used in production environments to ensure secure connections and data integrity.
  • Static Files. Kubernetes can authenticate users based on credentials stored in static files. This method is similar to using static passwords and tokens, but it allows for the storage of multiple user credentials in a file, which the API server reads at startup. It provides a simple and direct way to manage access for a fixed list of users and is often used in smaller or less dynamic environments.
  • External Authentication Providers. To enhance flexibility and integrate with existing enterprise systems, Kubernetes can delegate authentication to external authentication providers. This includes systems such as LDAP (Lightweight Directory Access Protocol), SAML (Security Assertion Markup Language), and OAuth2. Integration with these external systems allows Kubernetes to leverage robust, enterprise-grade user management and authentication infrastructures, providing centralized control over access and enabling features like multi-factor authentication and single sign-on (SSO).

Authentication Flowchart

Example commands for managing authentication include:

# Create a CSR for a new user with client certificate authenticationopenssl req -new -key user-key.pem -out user.csr -subj "/CN=user/O=group"
# View authorized tokenskubectl get secrets

#2 Authorization

Once authenticated, Kubernetes uses authorization to determine which actions an authenticated user can take. Kubernetes offers multiple types of authorization:

  • Role-Based Access Control (RBAC). This approach of access management grants permissions depending on roles within the Kubernetes environment. RBAC is highly flexible, allowing administrators to build finely granular access control settings. Roles, or sets of permissions, are allocated to users, groups, and service accounts. These permissions specify the roles’ actions, such as viewing, editing, or deleting resources. This strategy benefits large businesses with complicated processes and diverse user responsibilities, as it ensures that users can only access the information they need to execute their duties.
  • Attribute-Based Access Control (ABAC). ABAC enables or refuses actions depending on various criteria, including user traits, environmental attributes, resource categories, and actions. This approach offers great flexibility and granularity, allowing for the implementation of sophisticated security rules that match an organization’s specific security requirements. For example, a policy may restrict access to a resource to specified times of day or network locations, thus increasing security and control.
  • Webhook Mode. In webhook mode, Kubernetes calls an external service to determine access rights. This adaptable option allows Kubernetes to connect with external authorization systems that enforce their access requirements. The webhook delivers information about the request to the external service, which subsequently responds by allowing or denying the request based on its policies. This solution is especially beneficial for enterprises with an external access control system and wish to easily integrate it with Kubernetes, providing consistent enforcement of security policies across several platforms.

RBAC Configuration Diagram

Commands to manage RBAC include:

# Create a role with specified permissionskubectl create role developer --verb=create --verb=get --resource=pods
# Bind a role to a user within a namespacekubectl create rolebinding developer-binding --role=developer --user=user1 --namespace=dev

#3 Admission Control

Admission controllers are software modules that operate as gatekeepers, intercepting API requests for the Kubernetes server after authentication and authorization. They can change or reject requests to enforce policies. Standard admission controls are:

  • PodSecurityPolicy (PSP). PodSecurityPolicy is a Kubernetes feature that controls security-sensitive parts of pod setups. This policy governs permissions for pod creation and operations throughout the cluster, allowing administrators to enforce rules that limit the use of dangerous features such as privileged containers, prevent the use of host networking and file systems, restrict the injection of additional capabilities, and control access to volume types and filesystems. PSP reduces the risk of security vulnerabilities caused by misconfigured pods by specifying a set of constraints a pod must meet before deployment. This is especially important in contexts where security is critical, as it prevents potential escalation and exploits at the application level.
  • NodeRestriction. This Kubernetes admission controller restricts the kubelet from executing operations only on resources relevant to its node. This prohibits the kubelet from reading or altering resources assigned to other nodes, which is critical for the security and integrity of node operation. The NodeRestriction admittance plugin guarantees that kubelets follow the concept of least privilege, lowering the likelihood of an intranode security breach in which a compromised kubelet affects the rest of the cluster.
  • NamespaceLifecycle. This admission controller oversees the lifecycle of namespaces in the Kubernetes cluster. It inhibits the deletion of system-critical namespaces that are required for the cluster’s functioning and the creation of new objects in a namespace that is about to be deleted. This controller contributes to the cluster’s organizational order an9089d operational efficiency by controlling namespace lifecycles. It is critical for resource cleanup, namespace allocation, and preventing resource leaks, which can result in denial-of-service assaults or resource depletion.

Admission Control Workflow

Example command to enforce a security policy:

# Apply a PodSecurityPolicykubectl apply -f podsecuritypolicy.yaml

Now that we’ve covered the basics of Kubernetes Security Strategy, let’s dig further into our following topic: how to successfully secure your Kubernetes cluster. In this section, we’ll go over a variety of security methods and recommended practices for protecting your cluster from potential attacks and vulnerabilities.

How Do I Secure My Kubernetes Cluster?

Securing a Kubernetes cluster is a multifaceted strategy that necessitates the implementation of appropriate controls across many system components and layers to protect against unauthorized access and other security risks. This procedure includes creating network policies to limit traffic flow, implementing role-based access control (RBAC) to manage user permissions, and deploying security tools to monitor and respond to suspicious activity. To protect against vulnerabilities, the Kubernetes environment should also be updated and patched regularly. Ensuring data encryption in transit and at rest is critical to ensuring the confidentiality and integrity of the cluster’s data. Each of these stages is crucial for developing a solid security framework that protects your Kubernetes infrastructure from both internal and external. We will discuss these aspects in more detail in the following section.

#1 Control Access to the Kubernetes API

The Kubernetes API is the central interface for cluster administration hence security is crucial. It is critical to regulate and limit who can access the cluster and what activities they can take.

  • Use TLS for All API Traffic. The Kubernetes API is the central interface for cluster administration; hence, security is crucial. It is critical to regulate and limit who can access the cluster and what activities they can take.
# Check TLS settings in the Kubernetes API server configurationps aux | grep kube-apiserver | grep -- --tls-cert-file
  • Authentication Mechanisms. Depending on the cluster size and usage, choose an appropriate authentication mechanism, such as X.509 client certificates, static bearer tokens, or integrating with external identity providers like OIDC or LDAP.
# Example: List the current authenticated sessionskubectl get serviceaccounts

#2 Implement Role-Based Access Control (RBAC)

Role-Based Access Control (RBAC) is a method for regulating access to computer or network resources based on the roles of individual users within your enterprise.

  • Configure RBAC Policies. Set up roles and role bindings that define what operations are allowed for each user or group within the cluster. RBAC ensures that users have the minimum necessary access that their roles require.
# Create a role that includes permission to list pods and serviceskubectl create role example-role --verb=list --resource=pods,services --namespace=default
# Bind this role to a userkubectl create rolebinding example-binding --role=example-role --user=john.doe --namespace=default

#3 Secure Node and Pod Access

Kubelets, which run on each node, should have restricted access to ensure they can only perform actions required for their operation.

  • Enable Kubelet Authentication and Authorization. Make sure kubelets are authenticated and authorized before they can interact with the Kubernetes API.
# Example command to set kubelet authentication and authorizationkubelet --authentication-token-webhook=true --authorization-mode=Webhook

#4 Use Network Policies

Network policies define how groups of pods are allowed to communicate with each other and other network endpoints.

  • Define and Implement Network Policies. Create specific rules that govern the traffic between pods within your cluster to isolate and secure network traffic.
# Example network policy to deny all traffic except from the same namespaceapiVersion: networking.k8s.io/v1kind: NetworkPolicymetadata:  name: deny-cross-namespace  namespace: defaultspec:  podSelector: {}  policyTypes:  - Ingress  ingress: []
# Apply the network policykubectl apply -f deny-cross-namespace.yaml

#5 Audit and Monitor Cluster Activities

Keeping a close watch on the activities within your cluster is crucial for early detection of potential security incidents.

  • Enable Audit Logs. Set up audit logs to record actions taken on the API for analysis in the event of an incident.
# Example of enabling audit logs in Kuberneteskube-apiserver --audit-log-path=/var/log/kubernetes/audit.log --audit-log-maxage=30 --audit-log-maxbackup=10 --audit-log-maxsize=100

What Are the Four C’s of Kubernetes Security

The four C’s of Kubernetes security take an extensive strategy to protect cloud-native applications and their environments, from infrastructure to application. These levels of protection are crucial because they form an in-depth defense, ensuring that if one layer is compromised, the remaining layers contribute to total security. There are four C’s:

#1 Cloud (or Cluster)

This layer refers to the security of the underlying infrastructure on which the Kubernetes cluster operates. Whether your cluster is hosted on a public cloud provider like Gcore, or on-premises in your own data centers, securing this layer involves:

  • Verify that the infrastructure is configured correctly, such as with firewalls, private networks, and secure access restrictions.
  • Installing security patches and updates on your operating systems and physical servers.
  • The infrastructure is being monitored for threats and weaknesses.

#2 Clusters

Securing the Kubernetes cluster itself is crucial since it directly manages the containers and orchestrates their deployment and operation. This includes:

  • Configuring Kubernetes components securely, including the API server, etcd, kubelet, and network policies.
  • Using Role-Based Access govern (RBAC) to govern who has access to the Kubernetes API and what actions they can take.
  • Allowing audit logs to track what activities are occurring within the cluster.
  • To prevent vulnerabilities, Kubernetes should be updated and patched on a regular basis.

#3 Containers

Securing containers that run applications and their dependencies is essential to maintaining strong Kubernetes security. This procedure entails using trustworthy base images and running extensive vulnerability assessments to ensure the containers are as secure as feasible from the outset. Furthermore, setting security contexts for containers helps limit their privileges and access to host resources, reducing the risk of a compromise. It is also critical to ensure that containers only use the network and disk resources they require, reducing the attack surface. Finally, runtime security monitoring is needed to detect and prevent malicious activity within the containerized system, ensuring continuous protection against threats.

#4 Code

At the application layer, code security is crucial to ensuring the overall safety of applications running within containers. This security strategy entails using safe coding techniques to protect against vulnerabilities like SQL injection and cross-site scripting. Additionally, static and dynamic analysis techniques are critical in discovering potential security problems in code prior to deployment. Efficient secret management is also required to protect sensitive information from unauthorized users. Furthermore, encrypting data transported to and from the application protects sensitive information from interception and unwanted access.

By carefully addressing each of these layers, enterprises may improve the security of their Kubernetes systems and defend them from a variety of security risks. This approach emphasizes the importance of comprehensive security measures that consider all areas of system architecture and deployment.

Conclusion

Securing your Kubernetes cluster is crucial for protecting your infrastructure. Understanding and implementing Kubernetes security principles such as authentication, authorization, admission control, and network policies can help you build a strong defense against security threats. The four C’s of Kubernetes security (Cloud, Clusters, Containers, and Code) emphasize the layered security strategy required for complete protection. With these measures in place, you can protect your Kubernetes environment and prepare for future scalability and expansion. If you’re looking to leverage the benefits of Kubernetes without the complexities and cost escalations of managing it yourself, Gcore offers Managed Kubernetes, simplifying the process for companies and technical decision-makers.

Discover more with Gcore Managed Kubernetes

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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

How to Run Hugging Face Spaces on Gcore Inference at the Edge

Running machine learning models, especially large-scale models like GPT 3 or BERT, requires a lot of computing power and comes with a lot of latency. This makes real-time applications resource-intensive and challenging to deliver. Running ML models at the edge is a lightweight approach offering significant advantages for latency, privacy, and resource optimization.  Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. Step 1: Log In to the Gcore Customer PortalGo to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. Step 2: Go to Inference at the EdgeIn the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. Step 3: Choose a Hugging Face ModelOpen huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. Click on Files in the Space and locate the Docker option. Copy the Docker image link and startup command from Hugging Face Space. Step 4: Deploy the Model on GcoreReturn to the Gcore Customer Portal deployment page and enter the following details: Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest Startup command: python app.py Container port: 7860 Configure the pod as follows: GPU-optimized: 1x L40S vCPUs: 16 RAM: 232GiB For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.Step 5: Interact with Your ModelOnce the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.Powerful, Simple AI Deployment with GcoreGcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. Explore Gcore Inference at the Edge

10 Common Web Performance Mistakes and How to Overcome Them

Web performance mistakes can carry a high price, resulting in websites that yield low conversion rates, high bounce rates, and poor sales. In this article, we dig into the top 10 mistakes you should avoid to boost your website performance.1. Slow or Unreliable Web HostYour site speed begins with your web host, which provides the server infrastructure and resources for your website. This includes the VMs and other infrastructure where your code and media files reside. Three common host-related problems are as follows:Server location: The further away your server is from your users, the slower the site speed and the poorer the experience for your website visitors. (More on this under point 7.)Shared hosting: Shared hosting solutions share server resources among multiple websites, leading to slow load times and spotty connections during peak times due to heavy usage. Shared VMs can also impact your website’s performance due to increased network traffic and resource contention.VPS hosting: Bandwidth limitations can be a significant issue with VPS hosting. A limited bandwidth package can cause your site speed to decrease during high-traffic periods, resulting in a sluggish user experience.Correct for server and VM hosting issues by choosing a provider with servers located closer to your user base and provisioning sufficient computational resources, like Gcore CDN. Use virtual dedicated servers (VDS/VPS) rather than shared hosting to avoid network traffic from other websites affecting your site’s performance. If you already use a VPS, consider upgrading your hosting plan to increase server resources and improve UX. For enterprises, dedicated servers may be more suitable.2. Inefficient Code, Libraries, and FrameworksPoor-quality code and inefficient frameworks can increase the size of web pages, consume too many resources, and slow down page load times. Code quality is often affected by syntax, semantics, and logic errors. Correct these issues by writing clean and simple code.Errors or inefficiencies introduced by developers can impact site performance, such as excessive API calls or memory overuse. Prevent these issues by using TypeScript, console.log, or built-in browser debuggers during development. For bugs in already shipped code, utilize logging and debugging tools like the GNU debugger or WinDbg to identify and resolve problems.Improving code quality also involves minimizing the use of large libraries and frameworks. While frontend frameworks like React, Vue, and Angular.js are popular for accelerating development, they often include extensive JavaScript and prebuilt components that can bloat your website’s codebase. To optimize for speed, carefully analyze your use case to determine if a framework is necessary. If a static page suffices, avoid using a framework altogether. If a framework is needed, select libraries that allow you to link only the required components.3. Unoptimized Code Files and FontsEven high-quality code needs optimization before shipping. Unoptimized JavaScript, HTML, and CSS files can increase page weight and necessitate multiple HTTP requests, especially if JavaScript files are executed individually.To optimize code, two effective techniques are minification and bundling.Minification removes redundant libraries, code, comments, unnecessary characters (e.g., commas and dots), and formatting to reduce your source code’s size. It also shortens variable and function names, further decreasing file size. Tools for minification include UglifyJS for JavaScript, CSSNano for CSS, and HTMLminifier for HTML.Bundling groups multiple files into one, reducing the number of HTTP requests and speeding up site load times. Popular bundling tools include Rollup, Webpack, and Parcel.File compression using GZIP or Brotli can also reduce the weight of HTTP requests and responses before they reach users’ browsers. Enable your chosen compression technique on your server only after checking that your server provider supports it.4. Unoptimized Images and VideosSome websites are slowed down by large media files. Upload only essential media files to your site. For images, compress or resize them using tools like TinyPNG and Compressor.io. Convert images from JPEG, PNG, and GIF to WebP and AVIF formats to maintain quality while reducing file size. This is especially beneficial in industries like e-commerce and travel, where multiple images boost conversion rates. Use dynamic image optimization services like Gcore Image Stack for efficient processing and delivery. For pages with multiple images, use CSS sprites to group them, reducing the number of HTTP requests and speeding up load times.When adding video files, use lite embeds for external links. Standard embed code, like YouTube’s, is heavy and can slow down your pages. Lite embeds load only thumbnail images initially, and the full video loads when users click the thumbnail, improving page speed.5. No Lazy LoadingLazy loading delays the rendering of heavy content like images and JavaScript files until the user needs it, contrasting with “eager” loading, which loads everything at once and slows down site load times. Even with optimized images and code, lazy loading can further enhance site speed through a process called “timing.”Image timing uses the HTML loading attribute in an image tag or frameworks like Angular or React to load images in response to user actions. The browser only requests images when the user interacts with specific features, triggering the download.JavaScript timing controls when certain code loads. If JavaScript doesn’t need to run until the entire page has rendered, use the defer attribute to delay its execution. If JavaScript can load at any time without affecting functionality, load it asynchronously with the async attribute.6. Heavy or Redundant External Widgets and PluginsWidgets and plugins are placed in designated frontend and backend locations to extend website functionality. Examples include Google review widgets that publish product reviews on your website and Facebook plugins that connect your website to your Facebook Page. As your website evolves, more plugins are typically installed, and sometimes website admins forget to remove those that are no longer required.Over time, heavy and unused plugins can consume substantial resources, slowing down your website unnecessarily. Widgets may also contain heavy HTML, CSS, or JavaScript files that hinder web performance.Remove unnecessary plugins and widgets, particularly those that make cURL calls, HTTP requests, or generate excessive database queries. Avoid plugins that load heavy scripts and styles or come from unreliable sources, as they may contain malicious code and degrade website performance.7. Network IssuesYour server’s physical location significantly impacts site speed for end users. For example, if your server is in the UK and your users are in China, they’ll experience high latency due to the distance and DNS resolution time. The greater the distance between the server and the user, the more network hops are required, increasing latency and slowing down site load times.DNS resolution plays a crucial role in this process. Your authoritative DNS provider resolves your domain name to your IP address. If the provider’s server is too far from the user, DNS resolution will be slow, giving visitors a poor first impression.To optimize content delivery and reduce latency, consider integrating a content delivery network (CDN) with your server-side code. A CDN stores copies of your static assets (e.g., container images, JavaScript, CSS, and HTML files) on geographically distributed servers. This distribution ensures that users can access your content from a server closer to their location, significantly improving site speed and performance.8. No CachingWithout caching, your website has to fetch data from the origin server every time a user requests. This increases the load time because the origin server is another physical hop that data has to travel.Caching helps solve this problem by serving pre-saved copies of your website. Copies of your web files are stored on distributed CDN servers, meaning they’re available physically closer to website viewers, resulting in quicker load times.An additional type of caching, DNS caching, temporarily stores DNS records in DNS resolvers. This allows for faster domain name resolution and accelerates the initial connection to a website.9. Excessive RedirectsWebsite redirects send users from one URL to another, often resulting in increased HTTP requests to servers. These additional requests can potentially crash servers or cause resource consumption issues. To prevent this, use tools like Screaming Frog to scan your website for redirects and reduce them to only those that are absolutely necessary. Additionally, limit each redirect to making no more than one request for a .css file and one for a .js file.10. Lack of Mobile OptimizationForgetting to optimize for mobile can harm your website’s performance. Mobile-first websites optimize for speed and UX. Better UX leads to happier customers and increased sales.Optimizing for mobile starts with understanding the CPU, bandwidth, and memory limitations of mobile devices compared to desktops. Sites with excessively heavy files will load slowly on mobiles. Writing mobile-first code, using mobile devices or emulators for building and testing, and enhancing UX for various mobile device types—such as those with larger screens or higher capacity—can go a long way to optimizing for mobile.How Can Gcore Help Prevent These Web Performance Mistakes?If you’re unsure where to start in correcting or preventing web performance mistakes, don’t worry—you don’t have to do it alone. Gcore offers a comprehensive suite of solutions designed to enhance your web performance and deliver the best user experience for your visitors:Powerful VMs: Fast web hosting with a wide range of virtual machines.Managed DNS: Hosting your DNS zones and ensuring quick DNS resolution with our fast Managed DNS.CDN: Accelerate both static and dynamic components of your website for global audiences.With robust infrastructure from Gcore, you can ensure optimal performance and a seamless experience for all your web visitors. Keep your website infrastructure in one place for a simplified website management experience.Need help getting started? Contact us for a personalized consultation and discover how Gcore can supercharge your website performance.Get in touch to boost your website

How to Choose Between Bare Metal GPUs and Virtual GPUs for AI Workloads

Choosing the right GPU type for your AI project can make a huge difference in cost and business outcomes. The first consideration is often whether you need a bare metal or virtual GPU. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server, whereas a virtual GPU means you share GPU resources with other virtual machines.Read on to discover the key differences between bare metal GPUs and virtual GPUs, including performance and scalability, to help you make an informed decision.The Difference Between Bare Metal and Virtual GPUsThe main difference between bare metal GPUs and virtual GPUs is how they use physical GPU resources. With a bare metal GPU, you get a physical server with an entire GPU chip (or chips) installed that is completely dedicated to the workloads you run on the server. There is no hypervisor layer between the operating system (OS) and the hardware, so applications use the GPU resources directly.With a virtual GPU, you get a virtual machine (VM) and uses one of two types of GPU virtualization, depending on your or a cloud provider’s capabilities:An entire, dedicated GPU used by a VM, also known as a passthrough GPUA shared GPU used by multiple VMs, also known as a vGPUAlthough a passthrough GPU VM gets the entire GPU, applications access it through the layers of a guest OS and hypervisor. Also, unlike a bare metal GPU instance, other critical VM resources that applications use, such as RAM, storage, and networking, are also virtualized.The difference between running applications with bare metal and virtual GPUsThese architectural features affect the following key aspects:Performance and latency: Applications running on a VM with a virtual GPU, especially vGPU, will have lower processing power and higher latency for the same GPU characteristics than those running on bare metal with a physical GPU.Cost: As a result of the above, bare metal GPUs are more expensive than virtual GPUs.Scalability: Virtual GPUs are easier to scale than bare metal GPUs because scaling the latter requires a new physical server. In contrast, a new GPU instance can be provisioned in the cloud in minutes or even seconds.Control over GPU hardware: This can be critical for certain configurations and optimizations. For example, when training massive deep learning models with a billion parameters, total control means the ability to optimize performance optimization—and that can have a big impact on training efficiency for massive datasets.Resource utilization: GPU virtualization can lead to underutilization if the tasks being performed don’t need the full power of the GPU, resulting in wasted resources.Below is a table summarizing the benefits and drawbacks of each approach: Bare metal GPUVirtual GPUPassthrough GPUvGPUBenefitsDedicated GPU resourcesHigh performance for demanding AI workloadsLower costSimple scalabilitySuitable for occasional or variable workloadsLowest costSimple scalabilitySuitable for occasional or variable workloadsDrawbacksHigh cost compared to virtual GPUsLess flexible and scalable than virtual GPUsLow performanceNot suitable for demanding AI workloadsLowest performanceNot suitable for demanding AI workloadsShould You Use Bare Metal or Virtual GPUs?Bare metal GPUs and virtual GPUs are typically used for different types of workloads. Your choice will depend on what AI tasks you’re looking to perform.Bare metal GPUs are better suited for compute-intensive AI workloads that require maximum performance and speed, such as training large language models. They are also a good choice for workloads that must run 24/7 without interruption, such as some production AI inference services. Finally, bare metal GPUs are preferred for real-time AI tasks, such as robotic surgery or high-frequency trading analytics.Virtual GPUs are a more suitable choice for the early stages of AI/ML and iteration on AI models, where flexibility and cost-effectiveness are more important than top performance. Workloads with variable or unpredictable resource requirements can also run on this type of GPU, such as training and fine-tuning small models or AI inference tasks that are not sensitive to latency and performance. Virtual GPUs are also great for occasional, short-term, and collaborative AI/ML projects that don’t require dedicated hardware—for example, an academic collaboration that includes multiple institutions.To choose the right type of GPU, consider these three factors:Performance requirements. Is the raw GPU speed critical for your AI workloads? If so, bare metal GPUs are a superior choice.Scalability and flexibility. Do you need GPUs that can easily scale up and down to handle dynamic workloads? If yes, opt for virtual GPUs.Budget. Depending on the cloud provider, bare metal GPU servers can be more expensive than virtual GPU instances. Virtual GPUs typically offer more flexible pricing, which may be appropriate for occasional or variable workloads.Your final choice between bare metal GPUs and virtual GPUs depends on the specific requirements of the AI/ML project, including performance needs, scalability requirements, workload types, and budget constraints. Evaluating these factors can help determine the most appropriate GPU option.Choose Gcore for Best-in-Class AI GPUsGcore offers bare metal servers with NVIDIA H100, A100, and L40S GPUs. Using the 3.2 Tbps InfiniBand interface, you can combine H100 or A100 servers into scalable GPU clusters for training and tuning massive ML models or for high-performance computing (HPC).If you are looking for a scalable and low-latency solution for global AI inference, explore Gcore Inference at the Edge. It especially benefits latency-sensitive, real-time applications, such as generative AI and object recognition.Discover Gcore bare metal GPUs

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