Deep learning+AI
core advantag
DC(data centre)
GPU-accelerated data centers can offer breakthrough performance with fewer servers and lower power consumption, enabling faster insights and significant cost reduction. With the deep learning solutions brought by GPUs, you can train extremely complex deep learning models and solve your major challenges.
workstation
Access high-performance computing suitable for deep learning on your desktop. From virtual desktops, applications and workstations to optimized containers in the cloud, find everything you need to get started with on-site deep learning.
cloud
Cloud computing has brought about changes in various industries by making data centers more accessible to the public and facilitating the digital transformation of enterprises. By leveraging GPUs that are available to all major cloud service providers worldwide, one can draw assistance from the data stored in the cloud.
value embodiment
NO.1
A more suitable solution
From data centers to desktops to cloud computing, we meet the diverse needs of different users for various business scenarios, and customize solutions that are more closely aligned with the specific business requirements for our customers.
NO.2
A more suitable GPU for you
It has a large user base, including those from universities, research institutes, hospitals, and the field of artificial intelligence. By combining actual user cases, it provides GPUs that are more suitable for users.
NO.3
More diverse hardware architecture
From internationally renowned brands to domestic products, from a single GPU to multiple GPUs, we can offer users different types of high-performance hardware architectures.
NO.4
More convenient services
Four locations with five centers, providing nationwide technical support, ensuring prompt response services for users.
application scenarios
medical field
Deep learning technology can be applied to medical image recognition. For instance, convolutional neural networks (CNN) can be utilized for the preprocessing, model construction, training, evaluation and application of medical images, thereby assisting doctors in disease diagnosis and treatment plan formulation.
automatic drive
Deep learning technology can be applied to medical image recognition. For instance, convolutional neural networks (CNN) can be utilized for the preprocessing, model construction, training, evaluation and application of medical images, thereby assisting doctors in disease diagnosis and treatment plan formulation.
smart home
Deep learning technology is applied in smart homes. By learning users' daily habits, it can intelligently adjust the indoor environment and provide personalized living experiences. For instance, through technologies such as intelligent voice control and facial recognition, the automation of home devices can be achieved.
network finance
Deep learning plays a role in credit prediction and assessment, as well as in transaction risk warning. By analyzing a large amount of customer transaction data and historical fraudulent transaction behaviors, it can provide early warnings and alerts for customers.
artistic creation
Deep learning technology has also been applied to AI painting. By learning from a vast amount of paintings and image data, it can generate highly realistic and artistically appealing paintings. This provides new creative methods and tools for designers, artists, and painting enthusiasts.
Typical/Our Successful Case Studies
Typical case
Shenlian Circuit: AI Smart Factory Facilitates Intelligent Upgrade of PCB Manufacturing
NVIDIA Solution
NVIDIA offers comprehensive solutions in the fields of deep learning and artificial intelligence, covering core aspects such as data generation, model training, and inference deployment, and has optimized scenarios for local, cloud, and hybrid deployments.
drill
Whether you are building a neural network or preparing to train an AI system, you can learn all the necessary knowledge to start the deep learning training.
inference
AI inference refers to the process of deploying pre-trained AI models to generate new data, and it is also a crucial step where AI produces results and drives innovation in various industries.