What is the difference between NVIDIA H100 NVL and H100 SXM GPUs?
NVIDIA H100 NVL vs. H100 SXM GPUs: Key Differences Explained
NVIDIA's H100 GPUs are powerful accelerators designed primarily for artificial intelligence (AI), deep learning, high-performance computing (HPC), and large-scale data centers. Two prominent variants of the H100 GPUs are the NVIDIA H100 NVL and H100 SXM. Although sharing the same underlying architecture, they differ significantly in configuration, memory capacity, performance, and intended use cases.
Below is a detailed comparison covering essential differences and use-case scenarios to help you understand which GPU variant suits your needs best.
1. Form Factor and Installation
NVIDIA H100 NVL
- Form Factor: PCI Express (PCIe) Gen5 card-based form factor.
- Installation: Designed to easily fit into standard data center servers with PCIe slots, making it accessible for traditional server configurations and data center upgrades without specialized infrastructure.
NVIDIA H100 SXM
- Form Factor: SXM (Socket-based) module.
- Installation: Requires specialized NVIDIA HGX or DGX platforms. SXM GPUs are mounted onto baseboards, offering optimized cooling, power management, and NVLink communication for high-density GPU clusters.
2. GPU Interconnect and NVLink
NVIDIA H100 NVL
- NVLink support is available, but bandwidth per GPU pair is typically lower compared to SXM models.
- Designed primarily for scalability over PCIe-based servers, with NVLink bridging between pairs of GPUs in a dual-card configuration (NVL variant optimized specifically for large language model inference).
NVIDIA H100 SXM
- Offers advanced, high-bandwidth NVLink interconnectivity optimized for large-scale GPU clusters.
- Provides exceptionally high inter-GPU communication speeds, making it ideal for training massive deep learning models and large-scale HPC applications.
3. Memory and Bandwidth
NVIDIA H100 NVL
- Specifically optimized for large model inference workloads (e.g., LLM inference).
- Memory: Combines two GPUs into a single logical GPU instance with up to 188 GB (2x94 GB) of HBM3 memory.
- Memory Bandwidth: Offers extremely high memory bandwidth, optimized to accelerate inference of extremely large deep learning models.
NVIDIA H100 SXM
- Optimized for training and deep learning workloads requiring high memory bandwidth.
- Memory: Typically available in 80 GB configurations per GPU, HBM3 memory type.
- Memory Bandwidth: Extremely high, optimized specifically for training large-scale deep learning models, HPC, and data-intensive workloads.
4. Power Consumption and Cooling Requirements
NVIDIA H100 NVL
- Lower power consumption compared to SXM counterparts (350–400W per GPU).
- More straightforward cooling solutions suitable for conventional server racks.
NVIDIA H100 SXM
- Higher power consumption per GPU (700W+ per GPU), requiring robust cooling solutions.
- Often deployed in NVIDIA DGX and HGX platforms, which provide specialized liquid cooling or advanced air-cooling systems.
5. Use Cases and Target Applications
NVIDIA H100 NVL
- Optimized for inference workloads, particularly large language model inference (e.g., ChatGPT, GPT-4, LLaMA, Falcon, and similar models).
- Ideal for enterprises and data centers looking to deploy extremely large models in inference scenarios with cost-effective, scalable solutions.
NVIDIA H100 SXM
- Designed for deep-learning training workloads, HPC applications, and research-intensive computing.
- Commonly deployed in supercomputers, research clusters, AI training platforms (e.g., NVIDIA DGX H100), and large-scale GPU accelerators.
6. Performance Comparison
| Feature | NVIDIA H100 NVL | NVIDIA H100 SXM |
|---|---|---|
| GPU Form Factor | PCIe NVL dual-GPU configuration | SXM module |
| Memory | 188 GB total (2 x 94 GB) HBM3 | 80 GB per GPU (HBM3) |
| NVLink | Optimized NVLink bridge between GPU pairs | High-bandwidth NVLink for multi-GPU clusters |
| Power Consumption | ~350–400W per GPU | ~700W+ per GPU |
| Cooling | Standard air cooling (server racks) | Specialized cooling required (liquid or air) |
| Intended Use-Case | Large-scale inference, large language models | Deep-learning training, HPC, large AI models |
Example Scenario: Choosing the Right GPU
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If your primary workload is inference (deploying large language models like GPT-4 or ChatGPT):
- Choose NVIDIA H100 NVL: Its dual-GPU NVLink bridge and large, combined memory capacity are ideal for handling massive inference workloads efficiently and at scale.
-
If your primary workload is training large-scale deep learning models (e.g., training GPT, large recommender systems, or HPC applications):
- Choose NVIDIA H100 SXM: Its higher NVLink bandwidth, optimized form factor, and specialized cooling make it the best fit for intense, compute-heavy workloads.
Conclusion
In summary, the NVIDIA H100 NVL is optimized for AI inference workloads, especially large-scale LLM deployment, offering superior memory capacity and cost-effective scalability. The NVIDIA H100 SXM, on the other hand, excels in training scenarios and high-performance computing applications that demand exceptional interconnectivity, high bandwidth, and specialized infrastructure.
Selecting the right GPU variant depends on your specific workload, infrastructure capabilities, and performance requirements.