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Artificial Intelligence (AI) for Cybersecurity

  • By Gcore
  • September 20, 2023
  • 8 min read
Artificial Intelligence (AI) for Cybersecurity

Artificial intelligence (AI) has become an essential tool in the field of cybersecurity because it excels in key tasks such as malware detection, unauthorized access identification, and the prevention of phishing attacks, reducing human workload in the field. In this article, we will explore how and when AI enhances cybersecurity, look at the challenges these applications present, and understand future trends in AI-driven cybersecurity.

What Is AI for Cybersecurity?

Artificial intelligence enhances cybersecurity by offering smart, adaptable solutions that keep up with changing threats. It constantly learns and evolves, making it a strong line of defense for IT systems. AI can quickly analyze large amounts of past data to understand normal user behavior, device usage, and network activity. This allows AI to spot and react to unusual patterns, as well as identify new or complicated threats like zero-day malware and ransomware attacks.

For instance, an artificial intelligence system could spot a new kind of malware by noticing strange file behavior or unusual network connections among its large database of known attacks. Without AI, this kind of attack might go unnoticed or be hard to stop, since traditional systems are generally set up to deal with known threats.

AI significantly improves our ability to detect and handle cyber threats, even those that slip past traditional security measures. As a result, AI in cybersecurity represents a paradigm shift in cybersecurity—from simply reacting to threats, to actively preventing them.

Key Features of AI Cybersecurity

AI-based cybersecurity solutions have distinct advantages over traditional methods, offering three core features that set them apart:

  • Real-time detection: AI responds swiftly to both known and unknown threats as they unfold in real time. Traditional systems may rely on periodic scans or preset rules, but AI continuously monitors network activity, allowing it to identify and mitigate risks as they happen.
  • Continuous learning: AI constantly refines its capabilities by learning from new data. This constant evolution enables AI to recognize fresh types of attacks or slight variations of existing ones more effectively than traditional systems. Over time, the AI model becomes increasingly capable of identifying and neutralizing novel threats.
  • Deep understanding: AI can uncover complex threats and find intricate patterns that might elude human detection because its vast computational power allows it to sift through massive data sets, uncovering subtle anomalies or connections that might otherwise escape human attention.

How Is AI Used in Cybersecurity?

Artificial intelligence excels in several key areas of cybersecurity, including the ability to uncover complex relationships between different types of threats and enhancing the accuracy and consistency of threat detection. For example, AI can use the MITRE ATT&CK framework to create a straightforward, chronological visualization of an attacker’s actions, helping to illuminate the progression of a threat. Here’s an example of the visualization:

MITRE ATT&CK visualization

Artificial intelligence streamlines rapid response efforts by prioritizing alerts, assessing threat criticality, and speeding up threat response workflows. It automates routine tasks like log organization and scans, reducing human error and addressing the shortage of skilled cybersecurity professionals.

AI allows organizations to track complex, multi-step cyberattacks by pulling together data from various security platforms. For instance, a cybercriminal may initially infiltrate a network, explore its structure quietly, and then begin data theft. While each of these steps might trigger a warning on individual security platforms, AI combines these alerts to reveal the attacker’s complete strategy. This holistic view enables an organization to take decisive action to stop the attack completely.

Real-World Use Cases of AI in Security and Their Benefits

Utilizing AI-powered cybersecurity tools offers organizations a range of tangible benefits across its various use cases. Let’s take a look at six specific use cases.

Why AI for cybersecurity?

Transaction Monitoring for Fraud Prevention

AI analyzes extensive transaction and login data to detect anomalies indicative of fraudulent behavior or unauthorized access. For example, it can scan huge volumes of transaction and login data, flagging unusual activities like new login locations or rapid transactions as potential threats. By connecting seemingly isolated incidents, AI provides human analysts with automated root-cause analyses so that the relevant improvements to security can be made.

Intelligent Endpoint and Network Safeguarding

This means protecting devices connected to a network—computers, mobiles, and servers—and the network itself. For example, when an employee inadvertently downloaded malware onto their work computer, the company’s AI-based security system immediately detected the unusual behavior, flagged the threat, and isolated the affected device from the network, preventing the malware from spreading to other systems.

By learning from historical data, AI identifies malware threats and behavioral anomalies on individual devices. It also cross-references incoming network traffic against known threat indicators, flagging unusual activity swiftly and accurately.

Policy-Compliant Cloud Protection

Within cloud environments, AI monitors cloud configuration settings and permissions in real time, providing alerts for any deviations from security policies. This tightens cloud security, making it easier to comply with both internal guidelines and external regulations, such as the General Data Protection Regulation (GDPR,) the Health Insurance Portability and Accountability Act (HIPAA,) and the Payment Card Industry Data Security Standard (PCI DSS.)

Accelerated Threat Detection and Containment

AI reduces dwell time—the time between a breach starting and being contained—by rapidly analyzing large datasets to identify unusual activities. It cross-references these irregular activities with established threat frameworks like MITRE ATT&CK to make the information easy to read and act upon for human specialists. The resulting reduction in dwell time minimizes damage and yields considerable cost savings: The longer a breach goes undetected, the more damage it can cause, as attackers use this time to steal data, install malware, and disrupt operations. Damage translates into financial loss due to downtime and even regulatory fines.

For example, when a healthcare provider suffered a cyber breach, its AI security tools quickly flagged irregular data transfers and mapped them to the MITRE ATT&CK framework. The cybersecurity team was able to identify and contain the issue with speed and accuracy, minimizing the exposure of patient data and avoiding hefty HIPAA fines.

Data-Driven Risk Management and Security Posture

Due to its capacity for high-speed data analysis, AI significantly reduces both the mean time to detect (MTTD) and mean time to respond (MTTR) to security threats by automating the analysis of vast volumes of threat research data. Security analysts can therefore refocus their efforts from mundane tasks to high-level, strategic threat assessments.

For instance, when a major retail company is hit by a sudden surge of fraudulent transactions, its AI-driven security system quickly identifies the unusual activity so that security analysts can immediately investigate and halt the scam. The result? No financial loss or reputational damage occurs.

Data-Driven, Futureproof Security Protocols

Instead of only using a fixed set of rules for handling security issues, AI can change these rules based on new information. For example, if AI detects an increase in login attempts from a specific geographical location that historically hasn’t shown such activity, it might automatically adjust the security protocols to require additional verification steps for users logging in from that area.

In this way, AI helps security experts keep improving how they handle problems. As a result, the organization gains heightened credibility among customers, stakeholders, and partners for its ability to adeptly manage and mitigate security risks.

Overcoming Challenges in Implementing AI for Cybersecurity

Let’s delve into some challenges of AI for cybersecurity: ensuring AI transparency, addressing bias, and seamlessly integrating AI into existing security systems. We’ll also discuss how thee challenges are being overcome.

Overcoming cybersecurity challenges with AI

Transparency

Artificial intelligence models are often perceived as “black boxes” due to the limited insights available into their decision-making processes, resulting in concerns about their transparency. Understanding how an AI model flags suspicious network activity in cybersecurity is crucial because it helps to validate the model’s decisions, enable improvements, ensure regulatory compliance, build trust, and allow for effective human intervention when needed.

To address this, the industry is moving towards transparent algorithms, such as LIME and SHAP. LIME can demystify a specific AI decision by simulating a simpler model, while SHAP clarifies how each data feature influences the AI’s decision. These approaches are making AI more understandable and accountable.

Bias and Fairness Concerns in AI Models and Data

Bias in AI can lead to erroneous decisions and misjudged threats. For example, AI is trained to identify potentially malicious behavior based on historical data. If the training data primarily consists of cyberattack examples originating from specific geographic locations or IP address ranges, the AI model might develop a bias towards flagging any activity from those locations as malicious, even when it’s not.

To mitigate bias, organizations should focus on diverse and representative training data, rigorous preprocessing, and ongoing evaluation. Fairness-aware learning algorithms and model monitoring can help. Organizations are increasingly dedicating resources to ensure AI-driven cybersecurity solutions are reliable, equitable, and free from unfair discrimination.

Integrating AI Solutions with Existing Security Systems

Successfully integrating artificial intelligence solutions into existing security architectures is possible only when compatibility and organizational needs are accounted for. This may involve custom interfaces that communicate with legacy systems, API integration for real-time data exchange, or choosing AI solutions designed to work with existing firewalls and intrusion detection systems. For a seamless experience, steps like compatibility testing and middleware solutions are crucial. Done right, this integration amplifies overall security effectiveness, as discussed earlier.

Best Practices for AI Implementation in Security

The adoption of AI in cybersecurity requires a strategic and responsible approach, focusing on effectiveness, ethics, and alignment with organizational goals. These best practices serve as the foundation for success in navigating the dynamic landscape of AI for cybersecurity:

Develop an AI Integration Plan

Create a comprehensive plan that addresses specific security challenges. For example, to combat phishing attacks, tailor a machine learning algorithm to detect fraudulent email patterns. Ensure that the AI solution aligns with existing security processes.

Ensure Data Quality and Privacy

The quality of data used for AI training and operation is paramount because poor-quality or incomplete data can result in misidentified threats, false alarms, and potentially disastrous security vulnerabilities. As mentioned before, if training data is from a geographically limited area, real data from that area will be handled differently than real data from elsewhere in the world.

Implement robust data handling, validation, cleaning, and transformation processes. For example, use ETL (extract, transform, load) pipelines to pull raw data from various sources, validate it against pre-set rules for quality (e.g., no missing values or outliers,) clean any anomalies, and then transform it into a unified format suitable for machine learning algorithms.

Safeguard personal and sensitive information through encryption and access restrictions to prevent data-related issues. As an example, encrypt sensitive customer data using AES-256 encryption before storing it in the database, and set up role-based access control so that only designated staff, like system administrators or senior data analysts, can decrypt and view this information.

Establish an Ethical Framework

Develop clear principles, guidelines, and practices to prevent biases and ensure transparency in AI operations. This provides both ethical and operational boundaries, reducing the risk of unjust or misunderstood decisions. Address the potential for AI models to inadvertently learn biases from training data to avoid unfair or discriminatory outcomes when the AI model is deployed, which would affect the integrity of the security measures in place.

For example, an organization could adopt a framework like the AI Ethics Guidelines from the European Union, which sets principles on transparency, fairness, and data governance. The organization could then implement an “AI Ethics Audit” process that involves third-party evaluators reviewing the AI model’s decision-making patterns and training data to ensure compliance with these principles.

Regularly Test and Update

Given the rapidly evolving security landscape, continuously test and update AI models to ensure they remain effective. For instance, if an organization’s AI model is designed to detect ransomware based on network traffic patterns, regular testing might involve exposing the model to new ransomware techniques that mimic legitimate traffic, followed by retraining and updating the model if it fails to identify this new kind of threat.

The Future of AI in Cybersecurity

As advancements in AI and machine learning continue, their applications in cybersecurity will expand, delivering new solutions and refined tools. This evolution may also involve integration with emerging technologies like 5G and the Internet of Things (IoT,) enabling a fusion of data collection and intelligent decision-making to bolster security.

The influence of AI on the cybersecurity landscape extends to the job market. While artificial intelligence automation may render certain roles, such as manual monitoring, obsolete, it concurrently creates fresh opportunities for specialization. Emerging positions like AI security specialists, machine learning engineers, and threat intelligence analysts are gaining prominence. This shift encourages skill development and specialization in these burgeoning fields, contributing to a more dynamic and adaptive security industry.

Conclusion

Cybersecurity threats are constantly evolving, demanding advanced solutions to safeguard digital assets. Artificial intelligence is emerging as a key tool for analyzing and responding to these dynamic risks. By integrating AI into cybersecurity systems, companies can swiftly adapt their cyber defenses by rapidly identifying and responding to emerging threats. This approach adds to, and is sometimes superior to, traditional security measures, leveraging predictive analytics to proactively identify and mitigate potential risks before they escalate.

For organizations seeking an integrated platform to harness the full potential of AI and streamline their machine learning endeavors, Gcore’s AI IPU and GPU Cloud Infrastructure. With features like version control, dataset management, and widespread availability across multiple regions, Gcore’s AI Infrastructure stands poised to elevate AI capabilities and fortify cybersecurity defenses.

Start Protecting against Cyberthreats with Gcore

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On the other hand, time-sensitive use cases like fraud detection demand rapid processing, while cost may drive decisions for lightweight applications like chatbot development.This guide provides a comprehensive overview of the AI models supported on the Gcore platform, their characteristics, and their most effective use cases to help you choose the right model for your needs. Our inference solution also supports custom AI models.Large language models (LLMs)LLMs are foundational for applications requiring human-like understanding and generation of text, making them crucial for customer service, research, and educational tools. These models are versatile and cover a range of applications:Text generation (e.g., creative writing, content creation)SummarizationQuestion answeringInstruction following (specific to instruct-tuned models)Sentiment analysisTranslationCode generation and debugging (if fine-tuned for programming tasks)Models supported by GcoreGcore supports the following models for inference, available in the Gcore Customer Portal. Activate them at the click of a button.Model nameProviderParametersKey characteristicsLLaMA-Pro-8BMeta AI8 BillionBalanced trade-off between cost and power, suitable for real-time applications.Llama-3.2-1B-InstructMeta AI1 BillionIdeal for lightweight tasks with minimal computational needs.Llama-3.2-3B-InstructMeta AI3 BillionOffers lower latency for moderate task complexity.Llama-3.1-8B-InstructMeta AI8 BillionOptimized for instruction following.Mistral-7B-Instruct-v0.3Mistral AI7 BillionExcellent for nuanced instruction-based responses.Mistral-Nemo-Instruct-2407Mistral AI & Nvidia7 BillionHigh efficiency with robust instruction-following capabilities.Qwen2.5-7B-InstructQwen7 BillionExcels in multilingual tasks and general-purpose applications.QwQ-32B-PreviewQwen32 BillionSuited for complex, multi-turn conversations and strategic decision-making.Marco-o1AIDC-AI1-5 Billion (est.)Designed for structured and open-ended problem-solving tasks.Business applicationsLLMs play a pivotal role in various business scenarios; choosing the right model will be primarily influenced by task complexity. For lightweight tasks like chatbot development and FAQ automation, models like Llama-3.2-1B-Instruct are highly effective. Medium complexity tasks, including document summarization and multilingual sentiment analysis, can leverage models like Llama-3.2-3B-Instruct and Qwen2.5-7B-Instruct. For high-performance needs like real-time customer service or healthcare diagnostics, models like LLaMA-Pro-8B and Mistral-Nemo-Instruct-2407 provide robust solutions. Complex, large-scale applications, like market forecasting and legal document synthesis, are ideally suited for advanced models like QwQ-32B-Preview. Additionally, specialized solutions for niche industries can benefit from Marco-o1’s unique capabilities.Image generationImage generation models empower industries like entertainment, advertising, and e-commerce to create engaging content that captures the audience’s attention. These models excel in producing creative and high-quality visuals. Key tasks include:Generating photorealistic imagesArtistic rendering (e.g., illustrations, concept art)Image enhancement (e.g., super-resolution, inpainting)Marketing and branding visualsModels supported by GcoreWe currently support six models via the Gcore Customer Portal, or you can bring your own image generation model to our inference platform.Model nameProviderParametersKey characteristicsByteDance/SDXL-LightningByteDance100-400 MillionLightning-fast text-to-image generation with 1024px outputs.stable-cascadeStability AI20M-3.6 BillionWorks on smaller latent spaces for faster and cheaper inference.stable-diffusion-xlStability AI~3.5B Base + 1.2B RefinementPhotorealistic outputs with detailed composition.stable-diffusion-3.5-large-turboStability AI8 BillionBalances high-quality outputs with faster inference.FLUX.1-schnellBlack Forest Labs12 BillionDesigned for fast, local development.FLUX.1-devBlack Forest Labs12 BillionOpen-weight model for non-commercial applications.Business applicationsIn high-quality image generation, models like stable-diffusion-xl and stable-cascade are commonly employed for creating marketing visuals, concept art for gaming, and detailed e-commerce product visualizations. Real-time applications, such as AR/VR customizations and interactive customer tools, benefit from the speed of ByteDance/SDXL-Lightning and FLUX.1-schnell. FLUX.1-dev and stable-diffusion-3.5-large-turbo are excellent options for experimentation and development, allowing startups and enterprises to prototype generative AI workflows cost-effectively. Specialized use cases, such as ultra-high-quality visuals for luxury goods or architectural renders, also find tailored solutions with stable-cascade.Speech recognitionSpeech recognition models are essential for industries like media, healthcare, and education, where transcription accuracy and speed directly impact their efficacy. They facilitate:Accurate speech-to-text transcriptionLow-latency live audio conversionMultilingual speech processing and translationAutomated note-taking and content creationModels supported by GcoreAt Gcore, our inference service supports two Whisper models, as well as custom speech recognition models.Model nameProviderParametersKey characteristicswhisper-large-v3-turboOpenAI809 MillionOptimized for speed with minimal accuracy trade-offs.whisper-large-v3OpenAI1.55 BillionHigh-quality multilingual speech-to-text and translation with reduced error rates.Business applicationsSpeech recognition technology supports a wide range of business functions, all requiring precision and accuracy, delivered at speed. For real-time transcription, whisper-large-v3-turbo is ideal for live captioning and speech analytics applications. High-accuracy tasks, including legal transcription, academic research, and multilingual content localization, leverage the advanced capabilities of whisper-large-v3. These models enable faster, more accurate workflows in sectors where precise audio-to-text conversion is crucial.Multimodal modelsBy bridging text, image, and other data modalities, multimodel models unlock innovative solutions for industries requiring complex data analysis. These models integrate diverse data types for applications in:Image captioningVisual question answeringMultilingual document processingRobotic visionModels supported by GcoreWe currently support the following multimodal models:Model nameProviderParametersKey characteristicsPixtral-12B-2409Mistral AI12 BillionExcels in instruction-following tasks with text and image integration.Qwen2-VL-7B-InstructQwen7 BillionAdvanced visual understanding and multilingual support.Business applicationsFor tasks like image captioning and visual question answering, Pixtral-12B-2409 provides robust capabilities in generating descriptive text and answering questions based on visual content. Qwen2-VL-7B-Instruct supports document analysis and robotic vision, enabling systems to extract insights from documents or understand their physical surroundings. These applications are transformative for industries ranging from digital media to robotics.A multitude of models, supported by GcoreStart developing on the Gcore platform today, leveraging top-tier GPUs for seamless AI model training and deployment. Simplify large-scale, cross-regional AI operations with our inference-at-the-edge solutions, backed by over a decade of CDN expertise.Get started with Inference at the Edge 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

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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