NVIDIA H800 PCIe Graphics Card 80 GB

The H800 PCIe 80 GB is a professional graphics card by NVIDIA, launched on March 21st, 2023. Built on the 5 nm process, and based on the GH100 graphics processor, the card does not support DirectX. Since H800 PCIe 80 GB does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games. The GH100 graphics processor is a large chip with a die area of 814 mm² and 80,000 million transistors. Unlike the fully unlocked H100 SXM5 80 GB, which uses the same GPU but has all 16896 shaders enabled, NVIDIA has disabled some shading units on the H800 PCIe 80 GB to reach the product’s target shader count. It features 14592 shading units, 456 texture mapping units, and 24 ROPs. Also included are 456 tensor cores which help improve the speed of machine learning applications. NVIDIA has paired 80 GB HBM2e memory with the H800 PCIe 80 GB, which are connected using a 5120-bit memory interface. The GPU is operating at a frequency of 1095 MHz, which can be boosted up to 1755 MHz, memory is running at 1593 MHz.

Being a dual-slot card, the NVIDIA H800 PCIe 80 GB draws power from 1x 16-pin power connector, with power draw rated at 350 W maximum. This device has no display connectivity, as it is not designed to have monitors connected to it. H800 PCIe 80 GB is connected to the rest of the system using a PCI-Express 5.0 x16 interface. The card measures 268 mm in length, 111 mm in width, and features a dual-slot cooling solution.

Key Features:

1. Hopper Architecture for Breakthrough AI and HPC Performance

• CUDA Cores
  • The H800 features 16,896 CUDA cores, providing unparalleled computational power for parallel processing in machine learning, simulations, and rendering.
• 4th-Generation Tensor Cores
  • Optimized for AI and machine learning, these Tensor Cores accelerate mixed-precision computations like FP8, FP16, BFLOAT16, and INT8, making it ideal for training and inference workflows.
• Transformer Engine
  • A groundbreaking feature that boosts performance in transformer-based AI models, widely used in natural language processing (NLP) and generative AI.

2. 80 GB HBM3 Memory

• Massive Memory Capacity
  • The 80 GB of HBM3 memory ensures seamless performance for memory-intensive tasks like large-scale AI training, high-resolution simulations, and data analytics.
• High Bandwidth
  • Provides up to 35 TB/s of memory bandwidth, enabling efficient handling of large datasets and complex workloads.

3. Scalability and Multi-GPU Support

• NVLink and NVSwitch Support
  • Allows seamless multi-GPU setups by connecting multiple H800 GPUs for increased compute power and memory sharing in large-scale environments.
• Multi-Instance GPU (MIG)
  • Splits the H800 into up to 7 smaller instances, allowing multiple users or tasks to securely and efficiently share GPU resources.

4. Advanced AI Acceleration

• NVIDIA AI Enterprise Software
  • Fully compatible with NVIDIA’s software stack for AI, ensuring optimized performance for training, inferencing, and deployment.
• CUDA and CUDA-X Support
  • Delivers a robust foundation for developing and deploying GPU-accelerated applications.

5. Energy Efficiency and Cooling

• Optimized Power Usage
  • Designed for energy-efficient performance with a TDP of approximately 300W, reducing operational costs in data centers.
• Passive Cooling
  • Ideal for rack-mounted servers and dense computing environments, ensuring thermal stability during extended workloads.

6. PCIe 4.0 Interface

• High-Speed Data Transfer
  • The PCIe 4.0 interface ensures low latency and high throughput, optimizing performance for bandwidth-intensive applications.

Applications:

1. Artificial Intelligence and Machine Learning

• • AI Training: Accelerates training of large-scale models like GPT, BERT, and DALL-E, reducing training time while maintaining accuracy.
  • AI Inference: Delivers real-time inference capabilities for applications like recommendation systems, autonomous vehicles, and voice recognition.
  • Generative AI: Powers generative AI models, enabling rapid creation of text, images, and other media.

2. High-Performance Computing (HPC)

• • Scientific Simulations: Speeds up simulations in fields like climate modeling, genomics, astrophysics, and material science.
  • Numerical Analysis: Provides precise and scalable performance for engineering and mathematical computations.

3. Data Analytics and Visualization

• • Big Data Processing: Handles massive datasets for real-time analytics, enabling insights in industries like finance, healthcare, and retail.
  • Data Visualization: Powers advanced visualization of complex datasets, supporting decision-making in engineering, architecture, and research.

4. Media and Entertainment

• • Rendering and VFX: Delivers real-time ray tracing and accelerated rendering for 3D content creation, animation, and visual effects.
  • Video Processing: Supports multi-stream video transcoding for high-resolution content production and delivery.

5. Cloud and Virtualization

• • Virtual Desktops: Enhances GPU-accelerated virtual desktops for remote work, enabling smooth performance for creative and engineering applications.
  • AI as a Service: Powers AI services in cloud environments, allowing enterprises to deploy scalable and efficient AI solutions.

Why Choose the NVIDIA H800 PCIe Graphics Card 80 GB?

1. Unmatched AI Performance

1. • With 4th-gen Tensor Cores, Transformer Engine, and 80 GB of memory, the H800 is purpose-built for large-scale AI training and inference.

2. Exceptional Memory Bandwidth

1. • The 3.35 TB/s bandwidth ensures fast data access, enabling smooth performance for memory-intensive workloads like simulations and data analytics.

3. Energy-Efficient Design

1. • Optimized for performance-per-watt, the H800 minimizes energy consumption while delivering enterprise-grade performance.

4. Scalable for Large Workloads

1. • With support for NVLink, NVSwitch, and MIG, the H800 can scale compute and memory resources for complex, multi-user environments.

5. Enterprise-Grade Reliability

1. • Backed by NVIDIA’s certified drivers and robust AI Enterprise software stack, ensuring stability and compatibility for critical applications.

6. Versatility Across Industries

1. • Suitable for AI research, HPC, rendering, analytics, and cloud applications, making it a versatile choice for enterprises aiming to innovate.