Artificial intelligence (AI) is revolutionizing the way businesses communicate with their customers. From engaging customers with AI-powered chatbots to offering 24/7 support through intelligent systems, AI is more than a technological advancement; it’s now considered an essential part of modern customer service. This article will explore the multifaceted impact of AI in customer service, detailing its applications, benefits, and its vital role in enhancing customer loyalty and operational efficiency, all of which help businesses to stand out in today’s market.
What Is AI for Customer Service?
AI for customer service, or AI customer experience (CX,) enhances how businesses provide customer service by making technology-based interactions efficient by increasing self-service options and decreasing the need for human agents, even for complex tasks. Ultimately, the goal is to enhance key performance indicators (KPIs) like customer loyalty and engagement.
Examples of AI in customer service include:
- Self-service food-ordering kiosks
- Business call screening tools
- Product recommendation chatbots
- Predictive analytics for customer service agents
- Enhanced knowledge base search results
These artificial intelligence systems understand unstructured information in a way similar to humans: They learn from interactions with customers and apply this knowledge to future engagements. They can offer a level of personalization that feels like natural human communication. By performing repetitive customer service tasks and providing quick answers to simple inquiries, AI frees up human customer service agents to focus on the most complex or highly individualized issues.
How AI in Customer Service Works
AI customer service is a three-stage process: insights, customer interaction, and automation. To see how these interconnect, let’s consider the example of an e-commerce retailer, which we’ll call “RetailX.”
Stage One: Insights for Data-Driven Decisions
The first stage involves using automated algorithms to collect and interpret data from various relevant digital sources. This shows broad trends in customer behavior, enabling organizations to decide how AI customer service can be of use.
At RetailX, the AI system gathers data from various customer interactions, including website visits, online purchases, cookie records, and customer reviews. RetailX then uses AI algorithms to analyze customer activity, like the time spent on different product pages and reviews given for past purchases, to make sense of the data and generate a holistic customer profile. Natural language processing (NLP) facilitates the analysis of customer reviews and feedback, generating a detailed understanding of what customers think and want.
Imagine a RetailX customer who frequently browses athletic gear. AI detects this pattern and starts to predict what products might interest them in the future, setting the stage for targeted customer service interactions.
Stage Two: Enhancing Customer Interaction
Customer interaction is at the heart of a memorable customer experience. The second stage of the AI in customer service flow focuses on using personalized insights to create an individualized and engaging customer experience.
In e-commerce, AI-driven systems analyze factors about individual user behavior, such as the time spent on a page or interactions with item types, all in real time. A nuanced, instantaneous analysis lets the AI offer suggestions that are highly tailored to what you’re likely to want at that moment. The result is an engaging and effective shopping experience, increasing the chances of you making a purchase.
For example, when our athletic gear enthusiast logs back into RetailX, AI algorithms populate the homepage with sports equipment and clothing, based on the specific sporting interests and brands in which our individual has already shown interest. The personalization isn’t limited to the website; it extends to the mobile app, social media, and email marketing as well, suggesting products aligned with the customer’s historical data while providing a cohesive customer experience across multiple channels. RetailX continuously updates these recommendations based on new data as it’s interpreted, keeping the experience fresh and relevant.
RetailX leverages AI in Internet of Things (IoT) devices like smart home devices, to keep the customer experience cohesive and personalized across multiple touchpoints, For instance, if our athletic gear enthusiast uses a smart speaker that’s synced with RetailX, they could receive audible recommendations for new running shoes or hydration gear based on their recent activity data.
Stage Three: The Power of Automation
This leads us to automation, wherein AI takes over routine tasks based on the information it gathered in stages one and two, making operations more efficient and freeing human agents to tackle nuanced and complicated issues. Automation encompasses multiple customer service tasks that AI can perform.
Take the example of our RetailX customer finally ready to purchase a pair of running shoes. The moment they click “Buy,” AI springs into action. It conducts an instantaneous inventory check and manages payment processing, minimizing delays. If the customer has a question, AI customer service bots equipped with natural language processing can resolve straightforward queries, like order status or return policies. For complex questions that require emotional intelligence, raise truly novel issues, or require nuanced understanding, the system reroutes the issue to human customer service agents.
This automation not only expedites the transaction but also generates new data as interactions unfold. This fresh data is channeled back into the first stage, continually refining the customer experience.
AI in Customer Service Applications
Let’s look at exactly what roles AI can support or take over in the realm of customer service.
Chatbots and 24/7 Support for Customer Retention
AI chatbots make it economically feasible for smaller companies to offer round-the-clock assistance. They use natural language processing to understand and respond to customer queries in real time, contributing to higher customer retention rates. While it’s true that 24/7 support existed before AI, the technology makes it more efficient, cost-effective, and scalable.
For instance, if you operate an online travel agency, you could deploy a customer service chatbot that provides instant updates on flight delays or cancellations. By offering timely and accurate information of this kind, your agency builds trust and creates a valued service—qualities essential for retaining customers for future interactions.
Personalized Customer Experience
AI empowers brands to offer personalized experiences to their customers. Algorithms analyze past user behavior and interactions to generate tailored recommendations. This level of customization increases engagement and fosters long-term relationships.
A movie streaming platform could use AI to take the concept of personalized film suggestions to the next level, adapting to your changing preferences. Say you started kayaking as a new hobby. AI could discover this through your activity across online platforms, and might recommend a documentary movie about the sport. This elevates your movie-watching experience from customized to truly individualized, promoting continued subscription renewals.
Data-Driven Surveys for Dynamic Feedback
Traditional surveys have been a cornerstone in gathering consumer insights, but AI-powered surveys offer a nuanced approach. They adjust questions in real time based on a respondent’s prior answers. This iterative process ensures that each question adds a layer of depth, making the data collected highly specific.
For instance, if you frequently eat at a certain restaurant chain and consistently choose vegetarian options, AI would note your preference. The restaurant could customize a survey sent to you to ask questions only about their vegetarian offerings, generating focused and actionable data and creating a highly relevant survey, thus improving survey engagement and completion rates.
Sales and Marketing Through AI-Enhanced Conversion
Predictive analytics in sales and marketing are nothing new. However, AI continuously analyzes user behavior, streamlining the journey from browsing to purchase, without altering the essence of the sales funnel.
Imagine an e-commerce scenario where you add a smartphone to your cart. The AI system could suggest adding a case with an image of your favorite band, a complementary item that you’re likely to need with a personal preference element, making the entire process more targeted and efficient.
Facial Recognition for Security and Personalization
While conventional facial recognition methods rely on static algorithms, AI-driven facial recognition adapts to variables like lighting, angle, and even aging, enhancing its accuracy over time. This continuous learning capacity ensures that security remains stringent without the need for frequent manual updates. It serves as an additional layer of authentication, improving existing security measures while adding a touch of personalization to the customer journey.
In a banking scenario, ATMs with AI-augmented facial recognition could not only verify customer identity with higher accuracy but also adapt to changes like facial hair or glasses. Upon successful authentication, the system could offer a personalized dashboard tailored to the customer’s past behavior and preferences, improving user engagement.
Virtual Assistants Beyond Hands-Free Support
Voice-activated virtual assistants take the monotony out of routine tasks. They handle activities like appointment scheduling with speed, relieving human customer service representatives of the most basic tasks.
Take healthcare appointment scheduling as an example. An AI virtual assistant understands voice commands, enabling patients to effortlessly book, reschedule, or cancel their appointments. Humans can perform all these tasks, but by handing them over to AI, representatives are free to engage in face-to-face interactions in the healthcare setting without a phone continuously ringing in the background.
Content Optimization for Audience Resonance
AI brings precision to content creation and optimization. Algorithms sift through customer interactions to tailor content that speaks directly to an individual consumer’s needs and preferences.
An e-commerce platform could use these algorithms to automatically translate product descriptions into multiple languages. This tailors the customer’s shopping experience to their linguistic preferences, making the platform more accessible globally. It could also suggest blog posts that refer to product lines in which the individual customer is likely to be interested, based on their broader online activity.
Real-Time Feedback Analysis
Customer feedback is integral to business improvement. AI analyzes data from various channels in real time, pinpointing areas that warrant immediate attention—a task that humans can also perform, but less rapidly and less efficiently than AI.
Consider a hotel chain that applies AI algorithms to review customer feedback on room amenities. If multiple guests complain about the Wi-Fi quality, the hotel can prioritize this issue and determine whether it’s a general problem or relevant to specific rooms only, ensuring that future guests have a better experience.
Overcoming Challenges and Risks of AI in Customer Service
AI in customer service is an intricate field with distinct challenges. The information required for AI to function optimally is often scattered across various channels, making unification a significant hurdle. Some companies are also hesitant about AI’s cost and potential return on investment, while others struggle with misconceptions about its capabilities. The line between using artificial intelligence for positive customer impact and respecting privacy and data risks must be carefully drawn, with the technology fine-tuned for each specific application.
These are serious challenges, but there are ways to confront them:
- Address potential inaccuracies and misinformation: Reducing errors in communication is vital, but the ultimate goal is to enhance user satisfaction by creating experiences that resonate with human instincts and expectations. This is done by:
- Being vigilant about errors: Potential inaccuracies, if left unaddressed, can erode trust and damage the brand’s image. Implementing timely corrections is not just a technical necessity but a strategic tool to preserve the integrity of the information. Ensuring that the content is accurate reflects the brand’s commitment to quality and fosters a relationship of trust with the audience.
- Training AI systems: By using large and varied datasets of both text and code, AI can be taught to converse and engage with humans in ways that feel natural. This focused training is a means to bridge the gap between artificial intelligence and human connection.
- Ensure human oversight for AI: Balancing the capabilities of AI with human understanding is essential to maintaining integrity and empathy in automated interactions. This is achieved by:
- Combining AI with human insight: While AI provides the efficiency and speed of automation, human involvement remains important for ensuring accuracy and authenticity. For example, the integration of AI customer service chatbots with human customer support staff results in a system that is not only efficient but also personalized, compassionate and responsive.
- Transparency about data usage: Being open about data usage builds trust and also forms a vital part of a broader governance strategy. Laying out how information will be handled, protected, and leveraged assures stakeholders that their privacy is respected, promoting a relationship of trust and alignment with ethical business practices.
- Support for employee growth: Integrating AI into business operations can impact staff roles. By investing in training and new career paths, companies demonstrate their focus on employee well-being. This proactive approach also portrays the organization as resilient and innovative.
Conclusion
The integration of artificial intelligence in customer service opens new horizons for businesses to create exceptional customer experiences. As we embrace this transformative technology, it is crucial to consider the challenges and ensure human oversight for AI-generated content. Looking ahead, companies that strategically adopt AI in customer service will stand out as technology-savvy innovators, creating breakthrough experiences that strengthen customer-brand connections.
Ready to take your customer service to the next level? Explore Gcore’s AI solutions and revolutionize your customer experience! Our AI Infrastructure enables you to build, train, and deploy machine learning models for any use case. With cutting-edge frameworks and support for AI hardware, you can create personalized interactions, gain predictive insights, and boost customer satisfaction.
Find out which solution works best for your AI requirements.
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How to deploy DeepSeek 70B with Ollama and a Web UI on Gcore Everywhere Inference
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What is AI inference and how does it work?
Artificial intelligence (AI) inference is what happens when a trained AI model is used to predict outcomes from new, unseen data. While training focuses on learning from historical datasets, inference is about putting that learned knowledge into action—such as identifying production bottlenecks before they happen, converting speech to text, or guiding self-driving cars in real time. This article walks you through the basics of AI inference and shows how to get started.What is AI inference?AI inference is the application phase of artificial intelligence. Once a model has been trained on large datasets, it shifts from “learning mode” to “doing mode”—providing predictions or decisions from new data inputs.For example, an e-commerce platform with a model trained on purchasing behavior uses inference to personalize recommendations for each site visitor. Without re-training from scratch, the model quickly adapts to new browsing patterns and purchasing signals, offering instant, relevant suggestions.By enabling actionable insights, inference is transforming how businesses and technologies function, empowering relevance and instant responsiveness in an increasingly data-driven world.How does AI inference work? A practical guideAI inference has four steps: data preparation, model loading, processing and prediction, and output generation.#1 Data preparationThe first step involves transforming raw input—such as text, images, or numerical data—into a format that the AI model can process. For instance, customer feedback might be converted into numerical representations of words and patterns, or an image could be resized and normalized. Proper data preparation ensures that the AI model can effectively understand and analyze the input. For businesses, this means making sure that input data is clean, well-structured, and formatted according to the model’s requirements.#2 Model loadingOnce the input data is ready, the trained AI model is loaded into memory. This model, equipped with patterns and relationships learned during training, acts as the foundation for predictions and decisions.Businesses must make sure that their infrastructure is capable of quickly loading and deploying AI models, especially during high-demand periods. We simplify this process by providing a high-performance platform with global scalability. Your models are loaded and operational in seconds, whether you’re using a custom model or an open-source one.#3 Processing and predictionIn this step, the prepared data is passed through the model’s neural networks, which apply learned patterns to generate insights or predictions. For example, a customer service AI might analyze incoming messages to determine if they express satisfaction or frustration.The speed and accuracy of this stage depend on access to low-latency infrastructure capable of handling complex calculations. Our edge inference solution means data processing happens close to the source, reducing latency and enabling real-time decision making.#4 Output generationThe final stage translates the model’s mathematical outputs into meaningful insights, such as predictions, labels, or recommendations. These outputs must be integrated into business workflows or customer-facing applications in a way that’s easy to understand and actionable.We help streamline this step by offering APIs and integration tools that allow businesses to seamlessly incorporate inference results into their operations, so outputs are accessible and actionable in real time.A real-life exampleLet’s look at how this works in practice. Consider a retail business implementing AI for inventory management. The system continuously:Receives data from point-of-sale systems and warehouse scannersProcesses this information through trained AI modelsGenerates predictions about future inventory needsAdjusts order quantities and timing automaticallyAll of this happens in milliseconds, making real-time decisions possible. However, the speed and efficiency depend on choosing the right infrastructure for your needs.The technology stack behind inferenceTo make this process work smoothly, specialized computing infrastructure and software need to work together.Computing infrastructureModern AI inference relies on specialized hardware designed to process mathematical operations quickly. While training AI models often requires expensive, high-powered graphics processors (GPUs), inference can run on more cost-effective hardware options:CPUs: Suitable for smaller-scale applicationsEdge devices: For processing data locally on smartphones or IoT devices or other hardware closer to the data source, resulting in low latency and better privacy.Cloud-based inference servers: Designed for handling large-scale operations, enabling centralized processing and flexible scaling.When evaluating computing infrastructure for AI, businesses should prioritize solutions that address latency, scalability, and ease of use. Edge inference capabilities are essential for deploying models closer to end users, which optimizes performance globally even during peak demand. Flexible access to diverse hardware options like GPUs, CPUs, and advanced accelerators ensures adaptability, while user-friendly tools and automated scaling enable seamless management and consistent performance.Software optimizationThe efficiency of inference depends heavily on software optimization. When done right, software optimization ensures that AI applications are fast, responsive, and scalable, making them practical for real-world use.Look for the following to identify a solution that reduces inference processing time and supports optimized results:Model compression and optimization: The computational load is reduced and inference occurs faster—without sacrificing accuracy.Workload distribution and automation: This means that resources are allocated efficiently and cost-effectively.Integration: Look for APIs and tools that connect seamlessly with existing business systems.The future of AI inferenceWe anticipate three major trends for the future of AI inference.First, we’re seeing a dramatic shift toward specialized AI accelerators and custom silicon. New chips are being developed and existing ones optimized specifically for inference workloads. These purpose-built processors are delivering significant improvements in both performance and energy efficiency compared to traditional GPUs. This specialization is making AI inference more cost-effective and environmentally sustainable, particularly for companies running large-scale operations.The second major trend is the emergence of lightweight, efficient models designed specifically for inference. While large language models like GPT-4 showcase the potential of AI, many businesses are finding that smaller, task-specific models can deliver comparable or better results for their particular needs. These “small language models” (SLMs) and domain-adapted models are trained on focused datasets and optimized for specific tasks, making them more practical for real-world deployment. This approach is particularly valuable for edge computing scenarios where computing resources are limited.Finally, the infrastructure for AI inference is becoming more sophisticated and accessible. Advanced orchestration tools are automating the complex process of model deployment, scaling, and monitoring. These platforms can automatically optimize model performance based on factors like latency requirements, cost constraints, and traffic patterns. This automation is making it possible for companies to deploy AI solutions without maintaining large specialized teams of ML engineers.Dive into more of our predictions for AI inference in 2025 and beyond in our dedicated article.Accelerate inference adoption for your businessAI inference is rapidly becoming a differentiator for businesses. By applying trained AI models to new data, companies can make instant predictions, automate decision-making, and optimize operations across industries. However, achieving these benefits depends on having the right infrastructure and expertise behind the scenes. This is where the choice of inference provider plays a critical role. The provider’s infrastructure determines latency, scalability, and overall efficiency, which directly affect business outcomes. A well-equipped provider allows businesses to maximize the value of their AI investments.At Gcore, we are uniquely positioned to meet these needs with our edge inference solution. Leveraging a secure, global network of over 180 points of presence equipped with NVIDIA GPUs, we deliver ultra-fast, low-latency inference capabilities. Intuitively deploy and scale open-source or custom models on our powerful platform that accelerates AI adoption for a competitive edge in an increasingly AI-driven world.Get a complimentary consultation about your AI inference needs
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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