H800 80G SXM Graphics Card

As a leading China supplier, Telefly provides wholesale pricing, bulk purchase discounts, and a customized pricelist for enterprises looking to buy AI GPUs at the most competitive rates. With a years warranty, guaranteed quality assurance, and expert technical support, Telefly is your trusted partner for AI computing solutions.

Key Features of Telefly H800 80G SXM Graphics Card

80GB HBM2e High-Speed Memory for AI & HPC

The H800 80G SXM is equipped with 80GB of high-bandwidth HBM2e memory, enabling exceptional data transfer speeds and efficient deep learning model training. Whether you're working on AI-driven simulations, large language models (LLMs), or scientific research, this GPU delivers industry-leading performance.

Optimized for AI Training & Deep Learning

Built with powerful Tensor Cores, the Telefly H800 80G SXM accelerates deep learning frameworks such as TensorFlow, PyTorch, Caffe, and MXNet. This enables businesses to train complex AI models faster, making it an ideal solution for machine learning, natural language processing (NLP), image recognition, and speech synthesis.

SXM Form Factor for Maximum Performance

Unlike PCIe GPUs, the H800 80G SXM is designed for high-performance server architectures, offering higher power efficiency, optimized cooling, and seamless multi-GPU scalability. The SXM interface provides faster communication speeds, making it the preferred choice for large-scale AI clusters and data centers.

Multi-GPU Scalability for Enterprise AI Applications

The H800 80G SXM Graphics Card is built to support multi-GPU configurations, allowing AI researchers and enterprises to scale their AI training workloads efficiently. Whether you need to train massive neural networks or run real-time AI inference, this high-performance GPU delivers exceptional parallel computing power.

Energy-Efficient AI Computing for Large-Scale Deployment

The H800 80G SXM is designed with optimized power efficiency, ensuring lower energy consumption while delivering maximum AI acceleration. This is essential for cloud AI providers, scientific institutions, and enterprise data centers looking to maximize computing efficiency while reducing operational costs.