The Development of AI Infrastructure: Transitioning from On-Site to the Cloud and Edge
- By Gcore
- April 26, 2024
- 8 min read
AI infrastructure—a backbone of modern technology—has undergone a significant transformation. Originally rooted in traditional on-premises setups, it has evolved toward more dynamic, cloud-based, and edge computing solutions. In this article, we’ll take a look at the driving forces behind this shift, the impact it’s having on businesses big and small, and the emerging trends shaping the future of AI infrastructure.
How Has AI Infrastructure Evolved?
The rapid evolution of technology and the subsequent shift of AI infrastructure from on-premises to cloud, and then to edge computing, represents a fundamental change in the way we process, store, and access data. Let’s look at the history of AI infrastructure to show how this evolution took shape.
AI infrastructure was traditionally based on-premises, meaning that all the servers, storage, and networking supporting AI applications were located within the physical premises of an organization. To accomplish this goal, companies housed servers on which applications were directly installed and managed.
In 1997, cloud computing was first defined. It laid the foundation for what would become cloud AI infrastructure, allowing businesses to take advantage of powerful AI capabilities without making a substantial initial investment in physical hardware. Instead, cloud AI infrastructure is designed to support AI applications by providing them with the vast computational power and data management capabilities they require to function effectively in a centralized, internet-accessible environment.
Cloud AI infrastructure includes several essential components. Distributed processing, which involves dividing large datasets into smaller segments to be processed concurrently across multiple machines, can significantly enhance AI training speeds and computing power. However, this method demands robust network speeds and meticulous coordination to be effective. Despite these challenges, when successfully implemented, distributed processing far surpasses the capabilities of traditional single-server systems in handling complex computations.
Machine learning services streamline the development and deployment of AI models by providing tools that automate tasks like model training and inference. For example, instead of manually coding algorithms, developers can use these services to select pre-built models that meet their needs. APIs (application programming interfaces) and SDKs (software development kits) further simplify the integration process, allowing developers to easily enhance their applications with AI features. This means adding complex capabilities, such as image recognition or natural language processing, without the need to write extensive new code.
The compute infrastructure within the cloud can efficiently perform complex AI tasks, such as processing large datasets and running sophisticated algorithms. Additionally, monitoring and management tools are equipped with features like real-time analytics and automated alerts that help ensure AI systems function optimally. These tools can adjust system parameters automatically based on performance data, such as increasing computing power during high-demand periods or optimizing resource allocation to improve efficiency.
More recently, in 2020, the focus shifted to edge AI. This model shifts AI-driven inference processes to the point of need, whether on a local device or a nearby computer, thus reducing latency by avoiding the need to send data back and forth to distant servers or cloud systems. Training of AI models can occur centrally, as it does not impact the end-user experience with latency.
Regarding storage, while training datasets can be centralized, databases for retrieval-augmented generation (RAG) models, which interact dynamically with operational models, should be at the edge to optimize response times and performance.
How the Evolution of AI Infrastructure Impacts the Tech Stack
The choice of AI infrastructure—whether on-premises, cloud, or edge—can profoundly impact the various layers of an organization’s tech stack; the set of technologies, software, and tools used to develop and deploy applications, as well as the regulatory requirements that are designed to protect the data being handled.
A tech stack is essentially the building blocks of any software project, including AI. In the case of AI, it consists of three main layers:
- Applications layer: This layer is the interface where users interact with the software. It typically includes user-facing applications built on open-source AI frameworks, which are customizable to meet specific business needs and can also include general user-facing applications not directly linked to AI but enhanced by it.
Your choice of AI infrastructure affects the applications layer in the following ways:
- On-premises: Integration with other services can be complex and may demand custom solutions that slow down innovation.
- Cloud: Cloud-based AI simplifies application deployment with pre-built integrations and APIs, allowing you to connect your AI seamlessly with existing systems. This streamlines development and makes it easier to incorporate new features or data sources.
- Edge: Edge AI might limit the complexity of user-facing applications due to the lower processing power of edge devices. However, it can enhance applications requiring real-time data processing, like traffic management systems.
- Model layer: At this level, AI models are developed, trained, and deployed. It consists of checkpoints that power AI products, requiring a hosting solution for deployment. This layer is influenced by the type of AI used, whether general, specific, or hyperlocal, each offering a different level of precision and relevance.
Your choice of AI infrastructure impacts the model layer as follows:
- On-premises: Training complex models often requires significant investment in hardware, which cannot adjust flexibly to varying performance needs. If not fully utilized, this equipment incurs costs without adding value, and quickly changing or upgrading underperforming hardware can be challenging. This rigidity poses substantial risks, particularly for startups needing operational flexibility.
- Cloud: Cloud platforms offer easy access to vast computing resources for training even the most intricate models—ideal for startups. Additionally, cloud-based deployment allows for automatic updates across all instances, improving efficiency, while offering flexible offerings and pricing models.
- Edge: Limited processing power on edge devices might restrict the type of models you can train. However, edge AI excels in scenarios requiring low latency, like real-time anomaly detection in industrial equipment.
- Infrastructure layer: This layer consists of the physical and software components that provide the foundation for the development, deployment, and management of AI projects. This includes APIs, data storage and management systems, machine learning frameworks and operating systems. It is this layer that supplies the necessary resources to the applications and model layers.
Naturally, the AI infrastructure you choose directly affects the infrastructure layer itself as well:
- On-premises: Managing all hardware and software components in-house, including data storage and security systems, requires a dedicated IT team and involves managing the entire hardware lifecycle—from procuring spare parts and updating firmware to transitioning to new models and recycling old hardware.
- Cloud: Cloud providers handle the underlying infrastructure, freeing you to focus on core AI development. Cloud services offer built-in security features and readily available machine learning frameworks, reducing the need for in-house expertise.
- Edge: Managing a network of edge devices can be complex, requiring specific procedures for software updates and security patching, unlike centrally managed cloud solutions. However, edge AI can reduce the burden on your core infrastructure by processing data locally, minimizing data transfer needs.
On-Premises vs. Cloud vs. Edge AI
Now that you understand how AI infrastructure has evolved and its role within the tech stack, let’s compare the three infrastructure types to determine which might be the best fit for your organization.
| Infrastructure type | On-premises | Cloud | Edge |
| Definition | AI computing infrastructure located within the physical premises of the organization | AI services and resources offered on-demand via the internet from a cloud service provider’s data centers | Distributed computing that brings AI data collection, analysis, training, inference, and storage closer to the location where it is needed |
| Key components | Servers, storage systems, networking hardware | Virtual servers, scalable storage, networking technology | Edge servers, IoT devices, local networks |
| Advantages | Provides businesses with greater control over their infrastructure and data management, allowing for tailored security measures and compliance with specific industry standards. Enhances security and data sovereignty by keeping sensitive data within the company’s local servers, adhering to local privacy laws and regulations while reducing the risk of data breaches. | Allows for scalability, easily adjusting resources to meet fluctuating demands. Also offers flexibility, enabling users to customize solutions and scale services to fit their specific needs without developing code themselves, while significantly reducing upfront capital expenditure by eliminating the need for costly hardware investments. | Reduces the time it takes for data to be processed by analyzing it directly on the device, making it ideal for time-sensitive applications, such as autonomous vehicles or live video streaming. Also enhances data security and privacy by minimizing data transmission to the cloud, reducing exposure to potential cyber threats. |
| Limitations | Involves higher upfront costs due to the need for purchasing and maintaining hardware and software. Requires a dedicated IT team for regular updates and troubleshooting. Moreover, expanding capacity requires additional investments in physical infrastructure, which can be time-consuming and costly, inhibiting scalability. | Can introduce potential latency issues, especially when data centers are geographically distant. Also incurs ongoing operational costs, which can accumulate over time. Additionally, hosting data on external servers raises security concerns, including data breaches and privacy issues, requiring robust security measures to mitigate risks. | Due to the limited computational power of edge devices, only certain tasks can be performed, restricting the complexity of applications. The diversity of hardware and compatibility issues with deep learning frameworks may also complicate the development and deployment of edge AI solutions. Unlike cloud computing, which allows for universal updates via the internet, edge computing may require bespoke updating procedures for each device. |
| Impact on applications layer | Requires manual installation and management; complete control but complicates scaling and integration. | Enables flexible deployment and scalability; simplifies integration with APIs and services | Enhances real-time data processing; reduces bandwidth but may limit complexity due to device constraints |
| Impact on model layer | Significant hardware investment needed for model training; low latency for specific applications without internet dependency | Easy access to vast computing resources for training complex models; potential latency issues based on data center proximity | Low-latency processing that’s ideal for real-time applications; computational power limits the complexity of trainable models |
Benefits of Cloud and Edge AI
The shift towards cloud and edge AI is benefiting businesses across sectors in several ways:
- Improved scalability: As the AI needs of a business grow, these infrastructures can easily adjust to meet scalability demands. This is particularly beneficial for industries with fluctuating needs, such as retail. During busy shopping periods, cloud and edge AI can rapidly scale to manage the increased demand, ensuring a smooth customer experience.
- Cost-effectiveness: The ability to scale resources up or down as needed with cloud and edge AI ensures that businesses only pay for what they use, such as in the manufacturing sector, where edge AI is being used for predictive maintenance. Sensors detect potential equipment failures before they occur, preventing costly downtime and repairs.
- Real-time data processing: In the healthcare sector, wearable health monitors can use edge AI to evaluate real-time metrics such as heart rate and blood pressure. This could allow immediate action in emergency situations, potentially saving lives. That said, healthcare organizations using edge AI need to conduct thorough risk assessments and ensure their implementation adheres to HIPAA regulations.
- Enhanced performance: Cloud and edge AI provide quick, efficient data processing, though edge is faster than cloud, achieving a latency of 25 milliseconds or better, in some locations. This enables organizations to make data-driven decisions faster, as in the case of self-driving cars. Edge AI facilitates the processing of real-time road activities, from recognizing traffic signs to detecting pedestrians, ensuring a smoother and safer driving experience.
- Data privacy: Edge AI processes data near the source through a dedicated network, enhancing data privacy for applications that do not reside on end-user devices. This setup allows residents to manage smart-home devices like doorbells, HVAC units, and lighting systems with reduced data exposure, as less personal information is transmitted to centralized servers, thus safeguarding against potential data breaches.
When To Choose On-Premises AI
While we’ve highlighted the significant advantages of cloud and edge AI, it’s important to recognize that on-premises solutions might sometimes be the preferable choice for certain organizations. For instance, those developing autonomous vehicles may opt to keep their hazard detection capabilities on-premises to ensure the security of proprietary data.
As such, if you’re in the market for AI infrastructure, ask yourself these critical questions before choosing your infrastructure type:
- Is your business dealing with sensitive data that needs extra layers of security?
- Are there industry-specific regulations requiring you to process and store data in-house?
- Or perhaps, do you operate in areas with unstable internet and need your AI operations to run smoothly regardless?
If any of these questions apply to your organization, an on-premises AI solution could be your best bet. Such a solution offers greater control over your system, ensuring your operations are secure, compliant, and uninterrupted.
What Does the Future of AI Infrastructure Look Like?
Looking ahead, we can expect AI infrastructure that aims to resolve privacy, latency, and computational challenges, starting by increasing the number of parameters in large, general-purpose AI models. This approach aims to broaden the models’ capabilities, allowing them to tackle a wide array of tasks. We’re also seeing a trend towards creating smaller, more specialized models. These leaner models are designed to perform specific tasks with greater precision, speed, and efficiency, requiring fewer resources than their larger counterparts.
Increased Adoption of Hybrid Models
We’re moving toward a more integrated approach that combines the strengths of on-premises, cloud, and edge. Businesses could store sensitive data securely on-premises, use vast cloud computational power for heavy-duty processing, and leverage the edge for real-time, low-latency tasks. The beauty of this model lies in its flexibility and efficiency, ensuring businesses can tailor their AI infrastructure to their needs while optimizing costs and performance.
Advances in Edge Computing
Edge computing is set to become even more powerful and accessible. The aim is to equip even the smallest devices with significant processing and inferencing capabilities, reducing reliance on central servers and making real-time AI applications more feasible across the board. This trend indicates a future where AI is accessible to all, making technology more responsive and personal.
AI-Optimized Hardware
The demand for AI-optimized hardware is growing. Future AI infrastructure will likely include specialized processors and chips designed specifically to handle AI workloads more efficiently, including micro AI. These advancements could provide the necessary speed and power to support complex AI algorithms, enhancing the capabilities of both cloud and edge computing solutions.
Conclusion
As AI keeps advancing, the choice of the right infrastructure—on-premises, cloud, or edge AI—becomes key to enhancing the scalability, efficiency, and flexibility of AI applications. Thoroughly evaluating your business’s unique requirements and anticipated future technological advancements can empower well-informed decisions that optimize your AI capabilities and support your long-term goals.
If you’re interested in pushing your AI projects to the next level, Gcore’s AI Infrastructure might just be what you need. Designed specifically for AI and compute-intensive workloads, our solution uses GPUs, with their thousands of cores, to speed up AI training and handle the high demands of deep learning models.
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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
AI model selection simplified: your guide to Gcore-supported model selection
2024 has been an exceptional year for advancements in artificial intelligence (AI). The variety of models has grown significantly, with impressive strides in performance across domains. Whether it’s text or image classification, text and image generation, speech models, or multimodal capabilities, businesses now face the challenge of navigating an ever-expanding catalog of open-source models. Understanding the differences in tasks and metrics targeted by these models is crucial to making informed decisions.At Gcore, we’ve been expanding our model catalog to simplify AI model testing and deployment. As businesses scale their AI applications across various units, identifying the best model for specific tasks becomes critical. For example, some applications, like cancer screening, prioritize accuracy over latency. 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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