NVIDIA · March 2023
H100
NVL1
The NVIDIA H100 NVL1 is a high-performance GPU designed for data centers, targeting AI training and inference workloads. It is part of the Hopper architecture, offering significant improvements in performance and efficiency over its predecessors. The H100 NVL1 is optimized for large-scale AI models and high-performance computing tasks, making it a top choice for enterprises and research institutions.
Provider Marketplace
All Cloud Providers
Compute Performance
Architecture
Memory & VRAM
Connectivity & Scaling
Virtualization
Power & Efficiency
Physical Design
Thermals & Cooling
Software Ecosystem
Server & Deployment
System Compatibility
Benchmarks & Throughput
Structured Sparsity
Supported (up to 2x vs dense)
Transformer Throughput
Supported (Transformer Engine)
Multi-GPU Scalability
Scaling Efficiency
Scaling Characteristics
Workload Readiness
LLM Training
The H100 NVL1, based on the Hopper architecture, is highly suitable for training large language models, supporting up to 400B+ parameter models in a multi-node setup due to its high VRAM capacity and advanced interconnects.
LLM Inference
Optimized for high throughput inference with 4th-gen Tensor cores, providing excellent token-per-second performance and sufficient KV cache for large models.
Vision Training
Ideal for vision model training with its high computational throughput and efficient tensor operations, supporting large batch sizes and complex models.
Diffusion Models
Highly capable for diffusion models due to its large memory bandwidth and tensor core optimizations, allowing for efficient training and inference.
Multimodal AI
Well-suited for multimodal AI tasks, leveraging its advanced architecture to handle diverse data types and complex model architectures efficiently.
Reinforcement Learning
Excellent for reinforcement learning with its high parallel processing capabilities and fast memory access, enabling rapid environment simulation and model updates.
HPC / Simulation
Strong performance in HPC simulations with robust FP64 support, making it suitable for scientific and engineering applications requiring high precision.
Scientific Computing
Highly effective for scientific computing tasks, offering substantial computational power and memory bandwidth for data-intensive workloads.
Edge Inference
Less suitable for edge inference due to high power consumption and large form factor, better suited for data center deployments.
Real-Time Serving
Capable of real-time AI serving with low latency and high throughput, ideal for demanding AI applications in a data center environment.
Fine-Tuning
Highly efficient for full fine-tuning tasks due to its large VRAM and advanced tensor core capabilities, supporting complex model adjustments.
LoRA Efficiency
Efficient for LoRA techniques, providing sufficient memory and processing power to handle parameter-efficient tuning methods.
Market Authority
Key Strengths
The H100 NVL1 excels at AI training and inference, particularly for large language models and complex neural networks. Its advanced architecture and high memory bandwidth provide significant performance advantages in deep learning and scientific computing tasks, making it a preferred choice for demanding workloads.
Limitations
While the H100 NVL1 offers exceptional performance, it comes with a high power requirement and cost, which may be a consideration for some organizations. Availability can be limited due to high demand, and its advanced features may require specific software optimizations to fully leverage its capabilities.
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Expert Insight
The H100 represents a strategic leap in AI compute. When comparing cloud providers, consider not just the hourly rate, but also the interconnect bandwidth (InfiniBand/NVLink) and regional availability which can significantly impact total cost of ownership for large-scale training.