NVIDIA H100 CNX Graphics Card 80 GB

The H100 CNX 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 H100 CNX 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 H100 CNX 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 H100 CNX, which are connected using a 5120-bit memory interface. The GPU is operating at a frequency of 690 MHz, which can be boosted up to 1845 MHz, memory is running at 1593 MHz.

Being a dual-slot card, the NVIDIA H100 CNX draws power from an 8-pin EPS 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. H100 CNX is connected to the rest of the system using a PCI-Express 5.0 x16 interface. The card measures 267 mm in length, 111 mm in width, and features a dual-slot cooling solution.

Key Features:

1. Integrated ConnectX-7 NIC

• Built-In Networking
  • The H100 CNX integrates NVIDIA ConnectX-7 smart NIC, which provides ultra-fast network connectivity (up to 400Gb/s) directly to the GPU. This minimizes CPU overhead, accelerates data movement, and reduces overall system latency.
• GPUDirect RDMA
  • Supports GPUDirect RDMA (Remote Direct Memory Access), allowing data to move directly between the network and GPU memory without going through the CPU, significantly speeding up distributed computing workflows.

2. Hopper Architecture with Advanced Compute Power

• CUDA Cores
  • Packed with 16,896 CUDA cores, the H100 CNX delivers exceptional parallel processing power for compute-intensive applications like AI, simulations, and rendering.
• 4th-Generation Tensor Cores
  • Offers unprecedented performance for mixed-precision computations such as FP8, FP16, and BFLOAT16, delivering faster results in AI training and inferencing tasks.
• Transformer Engine
  • Specifically designed for transformer-based models (e.g., GPT, BERT), enabling faster training and deployment of natural language processing (NLP), generative AI, and recommendation systems.

3. 80 GB HBM3 Memory

• Massive Memory Capacity
  • The 80 GB of HBM3 memory provides the bandwidth and capacity to handle large datasets, high-resolution models, and complex simulations seamlessly.
• 35 TB/s Memory Bandwidth
  • Industry-leading memory bandwidth ensures rapid data access, optimizing performance for memory-intensive workloads.

4. Scalable Multi-GPU Performance

• NVLink and NVSwitch Support
  • Enables seamless integration of multiple GPUs for large-scale AI training, HPC clusters, and distributed workloads, allowing scalable compute power and shared memory.
• Multi-Instance GPU (MIG)
  • Splits the H100 CNX into up to 7 isolated instances, allowing multiple tasks or users to access GPU resources simultaneously without performance compromise.

5. Energy Efficiency

• Optimized Power Consumption
  • Designed for performance-per-watt efficiency, the H100 CNX consumes around 350W, making it cost-effective for data centers with heavy workloads.
• Passive Cooling Design
  • Engineered for rack-mounted servers, ensuring reliable thermal management during sustained workloads.

6. PCIe 5.0 Interface

• High-Speed Data Transfer
  • The PCIe 5.0 interface ensures low-latency, high-throughput performance for bandwidth-intensive applications, maximizing overall efficiency.

Applications:

1. Artificial Intelligence and Machine Learning

• • AI Training: Accelerates the training of large AI models like GPT-4, BERT, and DALL-E, enabling faster time-to-results for complex tasks.
  • AI Inference: Handles real-time inferencing for recommendation systems, chatbots, and voice recognition with reduced latency and high throughput.
  • Generative AI: Powers next-generation generative AI models, delivering rapid creation of text, images, and multimedia content.

2. High-Performance Computing (HPC)

• • Scientific Research: Enhances simulations in genomics, climate modeling, astrophysics, and material science by providing unmatched computational capabilities.
  • Numerical Analysis: Speeds up precision computations for engineering, financial modeling, and complex mathematical tasks.

3. Data Analytics and Big Data Processing

• • Real-Time Analytics: Handles massive datasets for industries like finance, healthcare, and retail, delivering actionable insights in real-time.
  • Data Visualization: Powers advanced visualization tools for engineering, architecture, and scientific research.

4. Media and Entertainment

• • 3D Rendering and Animation: Accelerates real-time ray tracing, rendering, and animation for media production and VFX workflows.
  • High-Resolution Video Processing: Enables 8K video editing, transcoding, and multi-stream processing for content creators and broadcasters.

5. Networking and Distributed Workloads

• • Cloud AI and HPC: Optimized for distributed workloads in cloud environments, enabling seamless scaling of AI and HPC services.
  • Edge AI and IoT: Ideal for low-latency AI inferencing and processing in edge computing devices and IoT networks.

Why Choose the NVIDIA H100 CNX Graphics Card 80 GB?

1. Integrated Networking for Enhanced Performance

1. • The ConnectX-7 smart NIC integration eliminates the need for separate networking components, reducing latency, simplifying architecture, and accelerating data movement for distributed workloads.

2. Exceptional AI and HPC Power

1. • With 4th-gen Tensor Cores, Transformer Engine, and 3.35 TB/s memory bandwidth, the H100 CNX is a leader in AI training, inferencing, and complex simulations.

3. Massive Memory Capacity

1. • The 80 GB HBM3 memory ensures smooth performance for memory-intensive applications, from AI models to large-scale scientific simulations.

4. Scalability and Flexibility

1. • Supports NVLink, NVSwitch, and MIG, enabling scalability across multi-GPU setups while allowing resource partitioning for concurrent tasks or users.

5. Energy Efficiency and Cost-Effectiveness

1. • Optimized for energy-efficient performance, reducing operational costs in enterprise and data center environments.

6. Enterprise-Grade Reliability

1. • Backed by NVIDIA’s robust software stack, including CUDA, NVIDIA AI Enterprise, and TensorRT, ensuring stable and reliable operation for mission-critical applications.

7. Streamlined Architecture

1. • The integration of compute and networking within the GPU reduces the complexity of AI infrastructure, enhancing overall system performance and efficiency.