Understanding AI as a Service (AIaaS): Exploring Its Types and Applications
- By Gcore
- May 15, 2024
- 8 min read
Artificial Intelligence as a Service (AIaaS) is revolutionizing how businesses access and utilize AI technologies. By offering scalable, cloud-based solutions, AIaaS eliminates the need for substantial upfront investments, making advanced AI capabilities accessible to companies of all sizes. AIaaS provides various tools to enhance operations, drive innovation, and improve decision-making, from chatbots and virtual assistants to machine learning frameworks and no-code platforms. This article discusses the multiple types of AIaaS, how they work, and their applications while addressing the challenges and future trends in this rapidly evolving field.
What is AI as a Service (AIaaS)?
Artificial Intelligence as a Service (AIaaS) is a cloud-based service that delivers access to diverse AI technologies and tools, eliminating the need for substantial initial investment in infrastructure. AIaaS allows companies to harness AI capabilities such as machine learning, natural language processing, and computer vision through third-party providers. This outsourcing model empowers organizations to rapidly and cost-effectively experiment with and implement AI solutions.
Key Components and Architecture of AIaaS
AIaaS architecture consists of several key components that work together to deliver AI capabilities as a service:
- AI Infrastructure. Cloud service providers like Gcore offer the foundational infrastructure, including storage, compute power, and networking. AIaaS also utilizes specialized hardware such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) to handle computationally intensive tasks like deep learning.
- Scalability. AIaaS platforms can easily scale resources up or down based on the needs of the business, ensuring that companies can handle varying workloads efficiently. As a business grows, AIaaS can scale with it, providing the necessary computational power and tools to support increased demands.
- Accessibility. AIaaS makes advanced AI tools and capabilities accessible to businesses of all sizes, including small and medium enterprises (SMEs) that may not have the resources to develop in-house AI solutions. Additionally, no-code/low-code platforms and pre-built APIs provide user-friendly interfaces, allowing users with limited technical skills to implement and benefit from AI technologies.
AI as a Service (AIaaS) offers organizations a flexible, scalable, and cost-effective approach to integrate artificial intelligence into their operations. Companies can profit from AI by employing third-party providers’ infrastructure and knowledge, allowing them to focus on their core business. In the next section, let’s talk about the Types of AI as a Service.
What are the Types of AI as a Service
As AI continues to transform sectors, AI as a Service (AIaaS) offers organizations a flexible and scalable approach to incorporate artificial intelligence into their operations. Here are the main types of AIaaS that businesses can utilize:
#1 Bots and Digital Assistants
Bots and digital assistants are AI-driven software entities designed to interact with users, answer queries, and perform specific tasks. These tools use natural language processing (NLP) to simulate human conversation, providing a more natural and engaging user experience.
Examples:
- Chatbots. These are widely used in customer service to handle inquiries, provide information, and troubleshoot issues. They can operate 24/7, offering timely support to customers.
- Virtual Assistants. Digital assistants like Siri, Alexa, and Google Assistant help users with tasks such as setting reminders, playing music, or checking the weather.
Use Cases:
- Customer Service. Bots can handle routine customer inquiries, freeing up human agents to focus on more complex issues. This enhances customer satisfaction and operational efficiency.
- Marketing. Virtual assistants can engage with customers, recommend products, and provide personalized experiences, boosting customer engagement and sales.
#2 Application Programming Interfaces (APIs)
APIs are sets of rules and protocols that allow different software applications to communicate with each other. In the context of AIaaS, APIs enable developers to integrate AI capabilities into their applications without the need to build AI models from scratch. APIs provide access to AI functionalities such as image recognition, sentiment analysis, and language translation. They allow developers to enhance their applications with AI features by making simple API calls.
Examples:
- Natural Language Processing (NLP) APIs. These can be used for tasks like sentiment analysis, entity extraction, and language translation.
- Computer Vision APIs. These enable applications to perform tasks like object detection, face recognition, and image classification.
#3 Machine Learning (ML) Frameworks
Machine learning frameworks are libraries and tools that provide the building blocks for developing, training, and deploying machine learning models. These frameworks simplify the complex processes involved in machine learning, making it more accessible to developers and data scientists. ML frameworks offer pre-built components and algorithms, allowing users to focus on the specific requirements of their models rather than the underlying mechanics.
Examples:
- TensorFlow. An open-source library developed by Google, TensorFlow is widely used for building and deploying machine learning models.
- PyTorch. Developed by Facebook, PyTorch is known for its flexibility and ease of use, particularly in research and prototyping environments.
#4 No-Code or Low-Code ML Services
No-code or low-code platforms are designed to make AI accessible to users without deep technical expertise. These platforms provide visual interfaces and pre-built templates, enabling users to create, train, and deploy AI models without writing code. No-code platforms offer drag-and-drop interfaces that allow users to assemble AI models using predefined modules. Low-code platforms may require minimal coding, but still significantly reduce the complexity compared to traditional development.
Benefits and Examples:
- Rapid Prototyping. These platforms allow businesses to quickly develop and test AI solutions, accelerating innovation.
- Data Analysis. Users can easily analyze data, create visualizations, and generate reports, making data-driven decision-making more accessible.
How AI as a Service (AIaaS) Works
By leveraging cutting-edge technology and specialized equipment, AI as a Service (AIaaS) provides organizations with an opportunity to incorporate artificial intelligence into their operations without incurring significant upfront investments. It’s all about getting into the process of preparing, customizing, and weaving AI power into the fabric of business operations, making it a game changer for firms trying to innovate and stay competitive.
Infrastructure and Technology Behind AIaaS
AI as a Service (AIaaS) leverages advanced infrastructure and technology to provide scalable and efficient AI solutions. The core components include:
- Cloud Computing. AIaaS primarily relies on cloud computing platforms provided by companies, for instance, here in Gcore. These platforms offer the necessary infrastructure to support AI operations, including vast storage, robust computing capabilities, and reliable networking. Cloud computing allows businesses to access and scale resources on demand, ensuring they only pay for what they use.
- Specialized Hardware. To handle the computationally intensive tasks associated with AI, AIaaS utilizes specialized hardware such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). GPUs and TPUs provide the high processing power required for training and running complex AI models efficiently, making it feasible to manage large-scale AI workloads.
- AI Frameworks and Libraries. AIaaS providers offer various frameworks and libraries, such as TensorFlow, PyTorch, and Keras, which simplify the development and deployment of machine learning models. These tools provide pre-built components and algorithms, enabling developers to build custom AI solutions without starting from scratch.
Steps Involved in Deploying AIaaS Solutions
Deploying AIaaS solutions involves several critical steps, ensuring that businesses can seamlessly integrate AI into their operations:
#1 Setup
The first step is to select an AIaaS provider and configure the necessary cloud infrastructure. This includes setting up cloud storage, compute instances, and networking components required for AI workloads. Providers like Gcore facilitate easy setup with intuitive interfaces and pre-configured environments. Preparing the data is crucial for AI model training. This involves collecting, cleaning, and organizing data to ensure it is suitable for machine learning. AIaaS platforms often provide tools for data preprocessing and management, making this step more efficient.
#2 Customization
Once the infrastructure is set up, businesses can select appropriate AI models from pre-built templates or create custom models using provided frameworks. The training process involves feeding the model with prepared data to learn patterns and make predictions. AIaaS platforms offer scalable compute resources, such as GPUs and TPUs, to expedite model training. Optimizing the model involves adjusting hyperparameters to improve performance. This step is essential for ensuring the AI model meets the desired accuracy and efficiency standards. Many AIaaS platforms provide automated tools to assist with hyperparameter tuning.
#3 Integration
After customizing and training the AI model, the next step is to integrate it with existing business systems. AIaaS platforms provide APIs that enable seamless connectivity between AI models and other software applications, facilitating smooth data flow and interaction. Deploying the AI model involves moving it from the development environment to production, ensuring it can start delivering real-time insights and automations. Continuous monitoring is necessary to track performance and make adjustments as needed. AIaaS platforms offer tools for monitoring and managing deployed models, ensuring they remain effective and efficient over time.
By following these steps, businesses may effectively adopt AIaaS solutions, leveraging the power of artificial intelligence to improve operations, generate innovation, and make better decisions. AIaaS is cloud-based, making it flexible and scalable, allowing enterprises to react to changing demands and market situations without considerable upfront expenditure.
Challenges of AIaaS
While AI as a Service (AIaaS) offers numerous benefits, businesses must also navigate several challenges to ensure successful implementation and operation. Key challenges include security and privacy concerns, vendor dependency, and transparency and ethical issues.
#1 Security and Privacy Concerns
One of the primary challenges of AIaaS is the risk associated with data sharing and third-party access. When businesses use AIaaS platforms, they often need to share sensitive data with external providers, which can expose them to potential data breaches and unauthorized access.
Risks and Mitigations:
| Risk | Mitigation Strategy |
| Data breaches due to third-party access | Implement robust encryption for data in transit and at rest |
| Unauthorized access to sensitive information | Utilize strict access controls and multi-factor authentication |
| Compliance with data protection regulations | Ensure providers comply with GDPR, HIPAA, and other relevant standards |
#2 Importance of Data Governance and Compliance
Effective data governance is crucial for ensuring that data is managed securely and in compliance with relevant regulations. Businesses must implement policies and procedures to protect data integrity, confidentiality, and availability.
#3 Vendor Dependency
Reliance on a single AIaaS provider can lead to vendor lock-in, making it difficult for businesses to switch providers or adapt to new technologies. This dependency can also result in higher costs and limited flexibility.
Issues and Strategies:
| Issue | Strategy to Mitigate Risk |
| Vendor lock-in | Choose providers that support open standards and interoperability |
| High switching costs | Negotiate flexible contract terms and ensure data portability |
| Limited flexibility in services | Opt for multi-cloud strategies to diversify risk |
#4 Transparency and Ethical Concerns
Understanding AI decision-making processes can be challenging, particularly when dealing with complex algorithms and models. This lack of transparency can lead to ethical issues, including biases in AI systems.
Challenges and Solutions:
| Challenge | Solution |
| Lack of transparency in AI decisions | Use explainable AI (XAI) techniques to make AI decisions more interpretable |
| Addressing biases in AI models | Implement bias detection and mitigation tools; ensure diverse training data |
| Ensuring ethical AI usage | Develop and adhere to ethical guidelines |
#5 Addressing Biases and Ensuring Ethical AI Usage
To ensure ethical AI usage, businesses must proactively identify and mitigate biases in AI models. This involves using tools and techniques to detect biases, ensuring diverse and representative training data, and adhering to ethical guidelines throughout the AI development and deployment process.
Future of AI as a Service (AIaaS)
The future of AI as a Service (AIaaS) is poised to bring significant advancements and new opportunities for businesses. Emerging trends, integration with other technologies, and predictions for growth and evolution highlight the potential of AIaaS to transform industries. Additionally, there are significant advantages of AI Infrastructure as a Service for training and inference.
Emerging Trends in AIaaS
Several emerging trends are shaping the future of AIaaS, making it more versatile and powerful:
- Managed Services. As AIaaS matures, more providers are offering managed services that take care of the entire AI lifecycle—from data preparation and model training to deployment and monitoring. This trend allows businesses to focus on their core competencies while leveraging advanced AI capabilities.
- Microservices. The adoption of microservices architecture in AIaaS is increasing, enabling more modular and scalable AI applications. Microservices allow businesses to deploy and manage individual components of AI solutions independently, leading to greater flexibility and easier maintenance.
Integration with Other Technologies
AIaaS is increasingly being integrated with other cutting-edge technologies, enhancing its capabilities and expanding its applications:
- Internet of Things (IoT). The integration of AIaaS with IoT devices allows for real-time data analysis and decision-making. AI can process data from IoT sensors to optimize operations, predict maintenance needs, and improve efficiency in various industries such as manufacturing, healthcare, and smart cities.
- Blockchain. Combining AIaaS with blockchain technology offers enhanced security, transparency, and trust in AI applications. Blockchain can be used to secure data used in AI training, ensure the integrity of AI algorithms, and provide auditable records of AI decisions. This integration is particularly valuable in sectors like finance, supply chain, and healthcare.
Predictions for the Growth and Evolution of AIaaS
The AIaaS market is expected to grow rapidly in the coming years, driven by increasing demand for AI capabilities and advancements in technology:
- Market Growth. According to various industry reports, the AIaaS market is projected to grow significantly, with a compound annual growth rate (CAGR) exceeding 25%. This growth is fueled by the rising adoption of AI across industries and the need for scalable, cost-effective AI solutions.
- Enhanced Accessibility. AIaaS is expected to become more accessible to businesses of all sizes, including small and medium enterprises (SMEs). The development of user-friendly interfaces and no-code/low-code platforms will enable more companies to leverage AI without requiring extensive technical expertise.
- Advancements in AI Capabilities. Continuous advancements in AI technologies, such as improved natural language processing (NLP), computer vision, and machine learning algorithms, will enhance the capabilities of AIaaS. These improvements will enable more sophisticated and accurate AI applications, driving further adoption and innovation.
- Focus on Ethical AI. As AIaaS becomes more prevalent, there will be a greater emphasis on ensuring ethical AI practices. Providers and businesses will need to address issues related to bias, transparency, and accountability in AI applications. This focus will lead to the development of tools and frameworks to promote ethical AI usage.
The future of AIaaS seems promising, with rising trends, technology integrations, and significant development on the horizon. Staying ahead of AIaaS advances allows businesses to maximize innovation, efficiency, and competitive advantage in various industries.
Conclusion
Companies can implement cutting-edge AI technology without incurring significant upfront expenses by leveraging cloud-based, scalable, and adaptable solutions provided by AI as a Service (AIaaS). While it has its own set of obstacles, such as security and ethical concerns, the benefits of AIaaS are enormous, including cost savings, scalability, and ease of access. Adopting AIaaS promotes innovation and increases operational efficiency, ensuring businesses remain competitive in today’s digital economy.
If you’re looking to take your AI projects to the next level, Gcore’s AI Infrastructure could be just what you need. Our approach, designed exclusively for AI and compute-intensive workloads, takes advantage of GPUs’ hundreds of cores to accelerate AI training and manage the high demands of deep learning models.
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These purpose-built processors are delivering significant improvements in both performance and energy efficiency compared to traditional GPUs. This specialization is making AI inference more cost-effective and environmentally sustainable, particularly for companies running large-scale operations.The second major trend is the emergence of lightweight, efficient models designed specifically for inference. While large language models like GPT-4 showcase the potential of AI, many businesses are finding that smaller, task-specific models can deliver comparable or better results for their particular needs. These “small language models” (SLMs) and domain-adapted models are trained on focused datasets and optimized for specific tasks, making them more practical for real-world deployment. This approach is particularly valuable for edge computing scenarios where computing resources are limited.Finally, the infrastructure for AI inference is becoming more sophisticated and accessible. Advanced orchestration tools are automating the complex process of model deployment, scaling, and monitoring. These platforms can automatically optimize model performance based on factors like latency requirements, cost constraints, and traffic patterns. This automation is making it possible for companies to deploy AI solutions without maintaining large specialized teams of ML engineers.Dive into more of our predictions for AI inference in 2025 and beyond in our dedicated article.Accelerate inference adoption for your businessAI inference is rapidly becoming a differentiator for businesses. By applying trained AI models to new data, companies can make instant predictions, automate decision-making, and optimize operations across industries. However, achieving these benefits depends on having the right infrastructure and expertise behind the scenes. This is where the choice of inference provider plays a critical role. The provider’s infrastructure determines latency, scalability, and overall efficiency, which directly affect business outcomes. A well-equipped provider allows businesses to maximize the value of their AI investments.At Gcore, we are uniquely positioned to meet these needs with our edge inference solution. Leveraging a secure, global network of over 180 points of presence equipped with NVIDIA GPUs, we deliver ultra-fast, low-latency inference capabilities. Intuitively deploy and scale open-source or custom models on our powerful platform that accelerates AI adoption for a competitive edge in an increasingly AI-driven world.Get a complimentary consultation about your AI inference needs
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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