Faster LLM & transformer training
High-throughput real-time inference
HBM3-powered massive bandwidth
Scalable multi-GPU support with NVLink
The NVIDIA H100 GPU combines 4th-generation Tensor Cores with a Transformer Engine to deliver breakthrough performance for modern AI and high-performance computing workloads.
Accelerate AI and HPC matrix operations with significantly higher performance and efficiency.
Dynamically manages precision to boost large language model training and inference speed.
Delivers ultra-fast, low-latency inference with power efficiency, scalability, and reliability.
MRun workloads on fully dedicated GPU hardware including NVIDIA H200, H100, A100, L40S, and RTX series GPUs. Get exclusive resources, consistent performance, and full control—ideal for AI training, LLMs, HPC, and production environments.
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The NVIDIA H100 GPU is engineered to dramatically accelerate both AI training and inference while meeting the demanding requirements of modern data centers. Its architecture enables faster model development, real-time deployment, and reliable operation at enterprise scale.
Accelerates large language model training compared to older-generation GPUs.
Delivers massive performance gains for real-time and large-scale inference workloads.
Built for power efficiency, scalability, and long-term reliability.
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The NVIDIA H100 GPU delivers breakthrough performance for next-generation AI and high-performance computing workloads. With massive memory, advanced architecture, and enterprise-ready features, it accelerates training and inference, scales across multiple GPUs, and handles ultra-large models with efficiency and reliability.
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Choosing the right GPU depends on the scale and complexity of your workloads. NVIDIA H100 delivers next-generation performance for large language models, advanced AI training, and high-performance computing, while NVIDIA A100 remains a reliable choice for established AI and ML workloads. This comparison highlights the key differences to help you select the GPU that best fits your performance, budget, and scalability needs.
| Feature | NVIDIA H100 | NVIDIA A100 | Recommendation |
|---|---|---|---|
| Architecture | Hopper | Ampere | Large AI & HPC |
| GPU Memory | 80 GB HBM3 | 40/80 GB HBM2 | Big datasets |
| Memory Bandwidth | 3.35 TB/s | 1.6 TB/s | Fast training |
| Tensor Cores | 4th Generation | 3rd Generation | AI/ML tasks |
| Transformer Engine | Yes | No | LLMs |
| FP64 Performance | Up to 60 TFLOPS | Up to 19.5 TFLOPS | HPC workloads |
| FP32 Performance | Up to 120 TFLOPS | Up to 19.5 TFLOPS | AI training |
| AI Training Speed | Up to 4× faster than A100 | Baseline | Large models |
| AI Inference Speed | Up to 30× faster | Baseline | Real-time AI |
| NVLink Bandwidth | Up to 900 GB/s | Up to 600 GB/s | Multi-GPU clusters |
| Best For | LLMs, deep learning, HPC, AI inference at scale | AI/ML training, HPC, deep learning | Enterprise & research |
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The NVIDIA H100 GPU sets the standard for next-generation AI and high-performance computing. Built for massive models, real-time inference, and enterprise-scale deployments, it delivers unmatched speed, efficiency, and reliability for even the most demanding workloads.
The NVIDIA H100 GPU accelerates the training of massive language models such as GPT, BERT, and other transformer-based architectures. Its high-bandwidth HBM3 memory allows it to efficiently handle extremely large datasets, reducing training time and improving model performance.
H100 enables ultra-low-latency inference for applications like chatbots, recommendation engines, and real-time analytics. Multi-GPU configurations ensure that large-scale deployments can deliver consistent, high-speed responses even under heavy workloads.
H100 is ideal for scientific computing, simulations, and complex modeling workloads. Its architecture is optimized for matrix-heavy operations and massive parallel computations, providing significant acceleration for demanding HPC tasks.
With Multi-Instance GPU (MIG) support, H100 can partition GPU resources for multiple simultaneous workloads. Its enterprise-ready design ensures power efficiency, reliability, and scalable deployment in modern data centers.
H100 excels in applications such as image synthesis, video generation, drug discovery, and AI research. The built-in Transformer Engine optimizes precision and maximizes throughput, enabling faster experimentation and deployment of AI models.
The H100 provides accelerated performance for high-resolution graphics rendering, video encoding/decoding, and real-time visual computing. Its massive memory and compute capabilities make it ideal for next-gen gaming engines, virtual production, and AI-enhanced graphics workflows.
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Get quick answers to the most common questions about the NVIDIA H100 GPU. Learn how its advanced memory, Hopper architecture, multi-GPU support, and enterprise-ready design accelerate AI training, inference, and high-performance computing workloads.
The H100 is built for next-generation AI, deep learning, large language models (LLMs), and high-performance computing (HPC), offering unmatched training and inference performance.
It features 80 GB HBM3 memory with 3.35 TB/s bandwidth, optimized for large models and massive datasets.
H100 uses 4th-Gen Tensor Cores and a built-in Transformer Engine to accelerate matrix operations and optimize large language model training.
Compared to previous-generation GPUs, it can deliver up to 4× faster AI training and up to 30× faster AI inference.
Yes, the H100 supports NVLink 4.0 and NVSwitch, enabling multi-GPU configurations and scaling for large workloads across nodes.
Absolutely — it’s designed for data-center deployment, with PCIe Gen5 support, 8-GPU configurations, power efficiency, and high reliability.
It supports Multi-Instance GPU (MIG) for workload partitioning, confidential computing for enhanced security, and AI model compression for efficient deployment.
The H100 is optimized for major deep learning frameworks, including PyTorch, TensorFlow, and others.
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