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AI Computing Power Cluster

VNET's AI Computing Power Cluster provides customized GPU computing power services and elastic computing services, boasting exceptional intelligent computing capabilities that can cater to various application scenarios such as artificial intelligence, large language model training and inference, deep learning, graphic visualization, and video processing, offering you robust computing power.

  • High demand for computing resources

    VNET's AI Computing Power Cluster provides customized GPU computing power services and elastic computing services, boasting exceptional intelligent computing capabilities that can cater to various application scenarios such as artificial intelligence, large language model training and inference, deep learning, graphic visualization, and video processing, offering you robust computing power.

  • Algorithm Efficiency Optimization

    Enterprises need to agilely adjust resource allocation and formulate usage strategies based on changes in their business, in order to achieve higher efficiency and thereby reduce the costs of training and inference.

  • Agile Deployment and Management

    Enterprises need to agilely adjust resource allocation and formulate usage strategies based on changes in their business, in order to achieve higher efficiency and thereby reduce the costs of training and inference.

VNET AIDC Solution

The GPU Computing Power Cluster model allows multiple clients to share the computing resources of the same cluster, dynamically allocating resources through logical isolation and distribution to effortlessly handle high-real-time, high-concurrency, and massive computing tasks as well as computing power invocations. VNET provides exceptional infrastructure delivery and operational support, helping clients maximize cost-effectiveness. Furthermore, by collaborating with numerous ecosystem partners, we build a diversified ecosystem market that caters to users' diverse, flexible, and inclusive computing power needs.

  • Rapid Deployment

    |Significantly reduces deployment costs: Eliminating the need for substantial human and material resources to set up complex infrastructure.

    |Enables rapid deployment of standardized environments: Providing clients with a stable and consistent operational environment.

    |Plug-and-play models: Allowing for immediate integration without cumbersome configuration and debugging, thereby facilitating rapid business launch.

    |Efficient for both new business launches and high-performance development: Leveraging GPU computing clusters to achieve business objectives in the shortest time possible, enhancing work efficiency and market competitiveness.

  • Excellent infrastructure and cost-effectiveness

    |Offers exceptional underlying infrastructure delivery and operational support.

    |A dedicated technical team ensures stable and reliable infrastructure, minimizing the risk of business disruptions caused by hardware failures or other issues.

    |Enables clients to maximize cost-effectiveness through shared cluster models and professional operations management.

    |Allows for pay-as-you-go pricing, significantly reducing upfront investment and ongoing operational costs.

  • Diversified Ecosystem Services

    |Build a diversified eco-market to realize BaaS and SaaS platform services oriented towards industries and clients.

    |Provide diversified product and business solutions for both B-end and C-end users, covering large-scale data analysis and model training.

    |Rich eco-services continuously drive innovation, bringing more value to users.

Scenarios

  • Artificial Intelligence

    Artificial intelligence has achieved rapid development through significant breakthroughs in neural networks, deep learning frameworks, and algorithm optimization. For large language models and Mixture of Experts (MoE) models with ultra-large numbers of parameters, key processes such as data preprocessing, model training and fine-tuning, and model inference all require efficient parallel computing capabilities, extremely high GPU interconnect bandwidth, and low-latency support for video memory and networks. The GPU computing cluster of VNET, consisting of thousands of mainstream high-performance computing units, possesses significant advantages in the field of intelligent computing.

  • Image and Video Rendering

    Image and video rendering requires GPUs to possess efficient rendering performance, powerful parallel computing capabilities, and optimized rendering processes to meet the demands of various rendering tasks and AI model computations. High-resolution and high-framerate support, ray tracing technology implementation, acceleration for professional application software, and multi-task processing for AI applications all place high demands on GPUs for image and video rendering. The GPU computing cluster of VNET provides efficient rendering performance and powerful parallel computing capabilities, effectively meeting the needs of image and video rendering tasks that are often periodic and non-continuous in nature.

  • Education and Scientific Research

    Small educational institutions and scientific research institutes need to carry out computational tasks related to research and teaching, such as experiments and projects for AI and data science courses, but they do not have fixed self-built computing resources. VNET's GPU computing power cluster, with its powerful parallel processing capabilities, serves scenarios such as AI training/inference, scientific computing, graphics and image processing, video encoding and decoding, providing super-strong parallel computing capabilities to effectively alleviate computational pressure and enhance business efficiency and competitiveness.

Exclusively for AI model training, rush for computing power

The customized cluster model is primarily targeted at enterprises with large-scale GPU computing power requirements. These enterprises typically have specific, high-intensity computing needs that necessitate substantial GPU resources to support their business or research activities. For instance, increasingly larger parameter models, MoE (Mixture of Experts) models, and the like, all require large-scale GPUs for loading, running, training, and inference. Consequently, a sufficient number of GPU servers within a single cluster are indispensable.

  • Built on the NVIDIA platform

    By adopting NVIDIA's computing power products and leveraging the world's most mature enterprise AI development platform - NVAIE (NVIDIA AI Enterprise), we aim to maximize the release of computing power efficiency.

  • Turn-key AI Infrastructure

    Adopting a fully managed, one-stop service model, VNET provides professional technical support from computing, networking, to storage and software. Clients do not need to worry about the complexity of deployment and operation, allowing them to fully focus on their business.

  • Seamlessly Hybrid Infrastructure

    Helping users achieve secure and high-speed interconnection services, enabling seamless integration between AIDC and other public clouds and data centers.

  • Professional Services

    With the support of a professional team and specialized software, we help clients maximize the utilization of computing resources and fully leverage the performance of AI computing power.

Scenarios

  • Autonomous Driving

    With the emergence of neural networks, traditional policy-based models in the autonomous driving industry are gradually being replaced by end-to-end models. Leveraging neural networks, various models are interconnected, enabling powerful data analysis and pattern recognition capabilities, which provide more accurate environmental perception and intelligent decision support for autonomous driving systems. VNET's GPU computing clusters have been widely applied in the autonomous driving field, providing critical IT infrastructure for efficient algorithm execution, rapid model training and optimization, real-time data processing, and decision support.

  • High-Performance Computing (HPC)

    High-Performance Computing (HPC) has widespread applications in crucial fields such as physical simulations, weather forecasting, bioinformatics computations, and scientific modeling. It leverages parallel computing, distributed computing, and cluster computing methods to process massive amounts of data in a short period, enabling high-performance scientific computations and engineering simulations. AI processors, with their thousands of CUDA cores, can deliver up to petaflops of floating-point performance, effortlessly tackling enormous datasets and intricate neural networks. They provide robust support for deep learning model training, natural language processing, graphics and image processing, scientific simulations, etc.

  • Financial Industry

    The financial industry requires rapid processing of vast amounts of transactions, and portfolio risk management involves extensive mathematical calculations along with the analysis of massive datasets to identify patterns and trends. Furthermore, algorithmic trading, market forecasting, and customer behavior analysis all necessitate the support of GPU computing power. Given the high throughput and parallel processing capabilities of GPU computing, its demand in the financial industry is increasing rapidly, especially in scenarios that require processing large amounts of data and performing complex computations.

Customize Your Exclusive AIDC

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