NVIDIA · 2022-03-27
H100
NVL
The NVIDIA H100 NVL variant is optimized for large language model inference, offering up to 5x performance improvement over NVIDIA A100 systems for LLMs up to 70 billion parameters. It features a PCIe form factor, NVLink bridge, and 188GB HBM3 memory for enhanced performance and scalability.
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 NVL, based on the Hopper architecture, is highly suitable for training large language models up to 400B+ parameters in a multi-node setup due to its high VRAM capacity and advanced interconnects.
LLM Inference
The H100 NVL excels in LLM inference with high token-per-second throughput and ample KV cache headroom, making it ideal for large-scale deployments.
Vision Training
With its advanced Tensor cores and substantial VRAM, the H100 NVL is highly efficient for training large vision models, supporting complex architectures and large batch sizes.
Diffusion Models
The H100 NVL is well-suited for diffusion models, offering high computational throughput and memory bandwidth necessary for training and inference of complex generative models.
Multimodal AI
The H100 NVL's architecture supports multimodal AI tasks efficiently, providing the necessary compute power and memory bandwidth for handling diverse data types simultaneously.
Reinforcement Learning
The H100 NVL is highly capable for reinforcement learning workloads, offering fast computation and high memory capacity to handle complex environments and large state spaces.
HPC / Simulation
The H100 NVL provides strong support for HPC simulations with its robust FP64 performance, making it suitable for scientific and engineering simulations requiring high precision.
Scientific Computing
With excellent double precision capabilities, the H100 NVL is ideal for scientific computing tasks that demand high accuracy and computational power.
Edge Inference
The H100 NVL is not optimized for edge inference due to its high power consumption and large form factor, making it more suitable for data center environments.
Real-Time Serving
The H100 NVL is highly efficient for real-time AI serving, offering low latency and high throughput for demanding applications.
Fine-Tuning
The H100 NVL is highly efficient for full fine-tuning tasks, leveraging its large VRAM and advanced architecture to handle extensive model updates.
LoRA Efficiency
The H100 NVL is also efficient for LoRA fine-tuning, providing sufficient memory and compute resources to support parameter-efficient training methods.
Market Authority
Cloud Adoption
NVIDIA has publicly confirmed H100 NVL adoption by Microsoft Azure and Oracle Cloud Infrastructure.
Research Citations
Limited; as of June 2024, few peer-reviewed papers explicitly cite H100 NVL due to its recent release.
GitHub Support
Some emerging support; select repositories (e.g., NVIDIA/DeepLearningExamples) mention H100 NVL compatibility, but widespread optimization is not yet prevalent.
Key Strengths
The H100 NVL excels at large-scale AI training and inference tasks, particularly in natural language processing and deep learning models. Its architecture is optimized for transformer models, offering significant performance improvements over previous generations. The GPU's high memory bandwidth and advanced tensor cores make it ideal for demanding computational workloads.
Limitations
The H100 NVL's high power requirements and need for advanced cooling solutions can be a limitation for some deployments. Additionally, its premium pricing and availability constraints may pose challenges for smaller organizations. Users should also consider the infrastructure investment needed to fully leverage its capabilities.
Also in the Lineup
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.