Why Choose NVIDIA H100 SXM for Maximum AI Performance

Intensive AI workloads like training or fine-tuning advanced LLMs like Llama 3.2 11B, Qwen2-72B and more demand massive computing power and efficiency. This makes it critical to choose the right GPU to optimise AI performance and associated costs. The NVIDIA H100 SXM is considered one of the best GPUs to tackle large-scale AI workloads. It is a cutting-edge GPU designed for AI, deep learning and high-performance computing (HPC). But what makes it an ideal choice to boost AI performance? Continue reading as we explore why the NVIDIA H100 SXM GPU is perfect for your intensive AI workloads. 

1. SXM Architecture for Power and Efficiency

The NVIDIA H100 SXM is built on the SXM architecture, which distinguishes itself from PCIe-based GPUs like the NVIDIA H100 PCIe. SXM allows for more efficient power distribution and higher throughput by minimising communication latency between the GPU and the system. This is especially crucial for AI workloads that require frequent data transfer between GPUs and other components, such as during distributed training tasks.

Also Read: Comparing NVIDIA H100 PCIe vs SXM: Performance, Use Cases and More

2. NVLink for Scalable AI Models

The NVIDIA H100 SXM is complemented by NVLink for efficient multi-GPU scaling. NVLink provides a direct interconnect between GPUs with a P2P throughput of 745 GB/s as supported by the SXM5 architecture. This is particularly beneficial for AI applications that involve massive parallel processing across multiple GPUs, such as large AI model training or distributed inference.

3. Performance Metrics for AI Inference and Training

The NVIDIA H100 SXM is equipped with exceptional specifications that enhance its capability for AI tasks:

• 80 GB HBM2e Memory: This high-bandwidth memory allows for quick access to large datasets, which is essential for training complex models. The extensive memory capacity ensures that data-intensive applications run smoothly without bottlenecks.
• 1,984 Tensor Cores: Designed specifically for AI workloads, these cores accelerate matrix operations and other tensor calculations critical to deep learning. This allows for faster model training and inference to work with larger models and datasets efficiently.
• 60 TeraFLOPs of FP64 Performance: The double-precision floating-point capability of the H100 SXM is crucial for tasks requiring high accuracy, such as scientific simulations and precise AI model training.
• 2,000 TOPs for AI Inference: With this level of performance, the H100 SXM can handle real-time AI inference tasks with minimal latency, making it an excellent choice for applications in areas like autonomous systems and real-time analytics.

4. Optimised Networking for AI Workloads

The networking capabilities of the NVIDIA H100 SXM are another reason it excels in high-performance AI environments. The configuration available through Hyperstack supports high-speed networking of up to 350Gbps for fast data exchange between compute nodes. The upcoming flavours of NVIDIA H100 SXM on Hyperstack will also support InfiniBand networking up to 400 Gbps for even faster connectivity for distributed workloads.

5. Flexible Storage Solutions

Hyperstack offers access to the NVIDIA H100 SXM with a range of storage configurations to suit various AI workloads based on your specific needs:

• Persistent NVMe storage: This option allows for long-term data retention, ensuring that your large datasets are readily available for training and analysis.
• Ephemeral storage: With large ephemeral storage capacities, the NVIDIA H100 SXM is well-suited for high-performance AI and HPC workloads that require quick access to temporary data.

Conclusion

The NVIDIA H100 SXM is the go-to choice for those looking to boost AI performance, particularly for large-scale AI models and intensive workloads. Its cutting-edge SXM architecture, NVLink for seamless scaling, massive memory and tensor cores deliver unmatched power and efficiency. With Hyperstack’s high-speed networking and flexible storage solutions for NVIDIA H100 SXM, your AI workflows will operate at peak performance. 

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FAQs

Why is the NVIDIA H100 SXM ideal for maximum AI performance?

The NVIDIA H100 SXM is ideal for AI training, fine-tuning and inference of large-scale models due to its exceptional speed, scalability and efficiency.

How does NVLink in the NVIDIA H100 SXM benefit AI model training?

The NVIDIA H100 specs for the SXM variant comes with NVLink that enables 600 GB/s bidirectional GPU-to-GPU communication for high-performance distributed AI workloads.

What storage options are available with the NVIDIA H100 SXM on Hyperstack?

Hyperstack offers persistent NVMe storage for long-term data retention and large ephemeral storage for temporary, high-speed access during AI training and analysis.

What is the NVIDIA H100 price?

The pricing for NVIDIA H100 SXM starts from $2.25/hour on Hyperstack. Check out our cloud GPU pricing here.