H800

Cloud GPU Pricing

H800 Pricing

Join AnyGPU VIP membership only 88% off

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H800

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VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now

H800

//d

VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now
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H800

联系客服

VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now
# #

H800

//h

VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now
# #

H800

//d

VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now
Hot Plan # #

H800

联系客服

VIP membership 88% off
  • CPU:16核
  • Memory：96G
  • Store：200G + Data store：200G
Buy now
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AnyGPU advantage

H800 advantage

Low cost

H800 compute can be used on demand, so the Low cost can be greatly reduced, and it is more cost-effective than traditional GPU servers, especially for small and medium-sized enterprises or individual users.

More stable

H800graphics cloud hosting can avoid performance bottlenecks caused by multiple users on the same physical server, and can also scale performance to meet growing demand.

Safer

H800 compute take various measures to ensure user data security, such as data encryption, access control, firewall, vulnerability scanning, and security audit measures to prevent data leakage, tampering, and loss.

More flexible

H800 Graphics card Resources on the cloud host can be dynamically allocated according to demand, and you can flexibly adjust according to actual business needs, without investing in hardware equipment such as graphics cards.

More efficient

H800 cloud host uses a professional graphics card, which can efficiently execute graphics rendering and processing algorithms to generate complex and realistic images in real time. It can provide excellent performance and user experience in virtual reality, animation, visual effects and other fields.

Large-scale

H800 cloud hosts can process large-scale data and are widely used in deep learning and artificial intelligence. Through GPU acceleration, the training time of deep learning models can be significantly shortened, and the convergence speed and accuracy of algorithms can be improved.

AnyGPU