Russian 
English 
German 
French 
Japanese 
Vietnamese 
The NVIDIA H20 is a data center GPU based on the Hopper architecture, specifically designed for AI inference, large-scale model computation, and cloud applications. It inherits key technologies from the H100 but is optimized for power efficiency and cost-effectiveness, making it an ideal choice for enterprise AI applications, especially for deploying large language models (LLMs) and AI inference tasks in cloud environments.
🔍 Detailed H20 Specifications
The H20 is built on the Hopper architecture and features 14,592 CUDA cores. It integrates Tensor Cores optimized for AI workloads and supports the Transformer Engine, enabling highly efficient deep learning acceleration.
For memory, the H20 is equipped with 96GB of HBM3 memory with an ultra-high bandwidth of 4.0TB/s, significantly improving data transfer speeds. It supports NVLink for multi-GPU interconnect and uses the PCIe 5.0 interface.
The power consumption (TDP) of H20 is only 350W, making it much more energy-efficient than the 700W power draw of the H100 while maintaining strong AI compute capabilities. In FP16 precision, the H20 delivers up to 900 TFLOPS, and it also supports FP8 for optimized AI inference.
📌 Applications of H20
1. AI Inference & Large Language Models (LLMs)
• Optimized for large AI models such as ChatGPT, Gemini, and Claude.
• Designed for fast, efficient inference in cloud environments.
• Reduces power consumption while maintaining high AI compute performance.
2. Cloud Computing & AI SaaS Services
• Ideal for deployment on AWS, Google Cloud, Alibaba Cloud, and other cloud platforms.
• Supports AI-based speech recognition, machine translation, and virtual assistants.
• Provides a scalable, cost-effective AI infrastructure.
3. Medical AI (Medical Imaging & Genomic Analysis)
• Enhances medical imaging recognition (CT/MRI analysis).
• Accelerates protein folding prediction (AlphaFold) and genetic sequencing.
• Reduces processing times for AI-driven diagnostics.
4. Autonomous Driving & Robotics
• Supports object detection, path planning, and environmental perception.
• Suitable for Level 4 and above autonomous driving.
• Powers AI models in industrial robots and smart factories.
5. Financial AI & Algorithmic Trading
• Enhances real-time fraud detection, quantitative trading, and risk assessment.
• Delivers low-latency AI processing for financial applications.
• Enables deep learning models to improve real-time decision-making.
📊 H20 vs. H100 vs. H200: Which One to Choose?
The H20 shares the Hopper architecture with the H100 and H200 but differs in performance, power consumption, and cost. It features 14,592 CUDA cores, whereas the H100 and H200 have 16,896 CUDA cores.
Regarding memory, the H20 is equipped with 96GB of HBM3 memory with a 4.0TB/s bandwidth, which surpasses the H100’s 80GB memory (3.35TB/s bandwidth) but is lower than the H200’s 141GB HBM3 memory (4.8TB/s bandwidth).
For AI compute performance, the H20 delivers up to 900 TFLOPS in FP16, while the H100 reaches 1,000 TFLOPS, and the H200 achieves 1,200 TFLOPS.
One of the key advantages of the H20 is its power efficiency, with a TDP of only 350W, making it much more energy-efficient than both the H100 and H200, which have a TDP of 700W.
Buying Recommendation:
• H20 is the best choice for cloud AI inference and large-scale deployments, offering a high cost-to-performance ratio and lower power consumption.
• H100 is suitable for both AI training and high-performance inference, making it the preferred option for on-premise data centers.
• H200 is designed for ultra-large-scale AI training, such as next-generation large models like GPT-5.
💡 Conclusion: H20 is the Best Choice for AI Inference & Cloud Computing
The NVIDIA H20 delivers H100-class AI performance with lower power consumption and a more cost-effective approach, making it perfect for cloud-based AI inference, virtual assistants, medical AI, autonomous driving, and financial AI applications.
📩 Looking to purchase AI GPUs or solutions? Contact us now!
📲 WhatsApp: +8618948189913 🚀