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GPU-as-a-Service
Context:
Bharti Airtel has announced that it will not be an early mover in the GPU-as-a-Service (GaaS) space, according to Vice Chairman and Managing Director Gopal Vittal. This decision was revealed during the company’s Q3 earnings call on February 7, 2025.
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- Airtel is placing its emphasis on growing its data centre business through Nxtra Data Ltd, which aims to support AI and data storage services, rather than GPU-as-a-service. The company is keen on expanding its presence in this sector.
- Vittal highlighted that Airtel is ready to launch its Fixed Wireless Access (FWA) service with standalone technology, which provides high-speed internet access.
- The service has been expanding rapidly, with over 2,000 cities covered, and about 1.9 million fiber-to-home passes added each quarter. Airtel has already reached 35 million home passes.
GaaS Overview
- GPU-as-a-Service (GaaS) is an emerging cloud service that allows users to rent GPUs for various heavy workloads, such as machine learning, deep learning, gaming, video editing, and high-performance computing.
- This service is part of the broader category of Infrastructure-as-a-Service (IaaS), allowing businesses to leverage high-performance computing resources without the need for expensive hardware investments or complex infrastructure management.
- Market: Offered by rivals like Jio Platforms, Tata Communications, and others, as well as specialised data centre companies like E2E Networks, Ctrls Datacenters, and Yotta Data Services.
- The global GPUaaS market was valued at approximately USD 3,354 million in 2023 and is projected to grow at a CAGR of 21.6% from 2024 to 2030. This growth is driven by the increasing demand for high-performance computing (HPC) in sectors such as AI, ML, data analytics, and cloud gaming.
- Key Market Players: Prominent cloud service providers like NVIDIA, Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are consistently improving their GPUaaS offerings to meet the increasing demand for high-performance computing solutions.
Key Features and Benefits
- Scalability: Users can easily scale GPU resources up or down based on project requirements, ensuring efficient use of computing power.
- Cost Efficiency: The pay-per-use model allows organisations to pay only for the GPU resources they consume, reducing overall expenses.
- Ease of Use: GaaS simplifies access to advanced computational capabilities, making it easier for data scientists and developers to work on machine learning, deep learning, and other data-intensive applications.
- Data Security: Cloud providers typically implement robust security measures to protect sensitive information, ensuring data privacy and compliance.
- Faster Time-to-Market: Immediate access to cutting-edge technology enables rapid prototyping and deployment, accelerating innovation and development cycles.
Applications
- Machine Learning and Deep Learning: GPUs can significantly accelerate the training of complex models on large datasets, improving model accuracy and iteration speed.
- Automotive – Used in ADAS (Advanced Driver Assistance Systems), self-driving tech, and simulations.
- Healthcare – GPU-powered medical imaging, drug discovery, and genomic analysis.
- Finance – Risk analysis, high-frequency trading, fraud detection.
- Real Estate – 3D rendering, virtual tours, and augmented reality (AR) applications.
- Data Processing and Analytics: Parallel computing capabilities of GPUs enhance the efficiency of big data processing tasks, such as sorting and filtering.
Recent Developments
- Rackspace launched its GPUaaS in November 2024, offering on-demand access to high-performance computing resources for AI, machine learning, and data analytics.
- Lenovo introduced TruScale GPUaaS to accelerate AI transformation and provide on-demand GPU resources.
- Singtel announced its plans to launch GPUaaS in Singapore and Southeast Asia in the third quarter of 2024, aiming to meet the growing demand for high-performance computing solutions.
Market Opportunities and Challenges
- The trend of cloud computing and virtualisation offers considerable growth potential for GPUaaS providers. As more industries shift to the cloud and require scalable GPU resources, businesses can capitalise on offering flexible pricing models for GPU usage.
- Despite rapid growth, challenges like data security concerns, regulatory compliance, and the availability of skilled professionals in HPC and AI may affect market adoption, especially in regions with stringent regulations.
