Solutions Engineer

Instant access to NVIDIA H100 SXM GPUs—ideal for training large language models and high-performance computing—with hourly pricing, global availability, and fast deployment. [Rent cloud GPUs](https://www.runpod.io/gpu-cloud) on [RunPod cloud for AI](https://www.runpod.io) to benefit from flexible scaling and cutting-edge performance without upfront investment. The H100 SXM's advanced Tensor Cores and Transformer Engine deliver up to 4x faster AI training, making it perfect for demanding AI applications.

## **Why Choose the NVIDIA H100 SXM**

The NVIDIA H100 SXM GPU, built on the advanced Hopper architecture, offers unparalleled performance for AI and machine learning workloads. With features like fourth-generation Tensor Cores and native FP8 precision, it dramatically accelerates AI training, making it an ideal choice and one of the [best GPUs for AI models](https://www.runpod.io/ai-faq/what-are-the-best-gpus-for-running-ai-models) for cutting-edge AI development and research.

### **Benefits**

* **Unprecedented AI Performance**  
  Equipped with fourth-generation Tensor Cores and a dedicated Transformer Engine, the H100 SXM supports FP8 precision, enabling up to 4x faster AI training compared to previous generations. For a detailed comparison, see [H100 NVL vs H100 SXM](https://www.runpod.io/compare/h100nvl-vs-h100sxm). This capability is crucial for training massive models like large language models (LLMs) and vision transformers with minimal loss in accuracy.  
* **High Memory Capacity and Bandwidth**  
  The H100 SXM features HBM3 memory with options for 80 GB and 96 GB capacities and delivers a massive bandwidth of 3.35–3.36 TB/s. This combination supports large-scale AI training and inference, handling extensive datasets and complex models with ease.  
* **Enhanced Multi-GPU Communication**  
  The SXM form factor, when compared to the PCIe variant ([H100 PCIe vs H100 SXM](https://www.runpod.io/compare/h100pcie-vs-h100sxm)), provides superior GPU-to-GPU communication via NVLink, essential for distributed training and parallel workloads. This ensures near-linear scaling across multiple GPUs, maximizing performance efficiency in multi-GPU setups.  
* **Flexible Resource Utilization**  
  With Multi-Instance GPU (MIG) technology, the H100 SXM can be partitioned into up to 7 instances, each with isolated compute and memory resources. This feature supports multi-tenant AI workloads and flexible deployment scenarios, optimizing GPU utilization.  
* **Cost-Effective Access to Cutting-Edge Hardware**  
  Renting H100 SXM GPUs offers access to state-of-the-art hardware without the hefty capital investment. Understanding the [price of an NVIDIA H100](https://www.runpod.io/ai-faq/what-is-the-price-of-an-nvidia-h100-gpu) and reviewing the [GPU instance pricing](https://www.runpod.io/pricing) for rentals highlights the cost savings, allowing organizations to leverage high-performance capabilities for AI development and research cost-effectively.

## **Specifications**

| Feature | Value   |
| :---- | :---- |
| Architecture | Hopper (GH100) |
| Process Technology | TSMC’s 5nm |
| Transistor Count | 80 billion |
| Die Size | 814 mm² |
| Memory Capacity | 80 GB and 96 GB HBM3 |
| Memory Bandwidth | 3.35–3.36 TB/s |
| FP64 Performance | 34 TFLOPS (67 TFLOPS with Tensor Core) |
| FP32 Performance | 67 TFLOPS |
| Tensor Core TF32 Performance | 989 TFLOPS |
| BFLOAT16/FP16 Tensor Cores | 1,979 TFLOPS |
| FP8 Tensor Core Performance | 3,958 TFLOPS |
| INT8 Tensor Core Performance | 3,958 TOPS |
| Base Clock Speed | \~1,590–1,665 MHz |
| Boost Clock Speed | \~1,837–1,980 MHz |
| Thermal Design Power (TDP) | Up to 700W |
| Multi-Instance GPU (MIG) Support | Up to 7 instances per physical GPU |
| System Interface | PCIe 5.0 x16 |
| Decoders | 7 NVDEC, 7 JPEG |

For a comprehensive understanding of the [FLOPS performance of the H100](https://www.runpod.io/ai-faq/what-is-the-flops-performance-of-the-nvidia-h100-gpu) and details on the [power consumption of the NVIDIA H100](https://www.runpod.io/ai-faq/what-is-the-power-consumption-of-the-nvidia-h100-gpu), refer to our detailed FAQs.

## **FAQ**

### **What is the minimum rental duration for H100 SXM GPUs?**

Minimum rental durations vary by provider. Some offer hourly on-demand pricing, while others may require minimum commitments of days or weeks. For example, Hyperstack offers both on-demand and long-term reserved options, with potential discounts for longer commitments.

### **How are data handling and security procedures managed?**

Cloud providers offering H100 SXM rentals typically implement robust security measures, including data encryption at rest and in transit. Look for providers with SOC 2, ISO 27001, or other relevant certifications. The H100 itself supports hardware-level confidential computing features, adding an extra layer of security for sensitive AI workloads.

### **What happens in case of hardware failure?**

Reputable providers have redundancy and failover protocols in place. In the event of hardware failure, your workload should be automatically migrated to functional hardware. Always check the provider's SLA for specific guarantees and compensation policies related to downtime or hardware issues.

### **How do I choose between on-demand and reserved instances?**

On-demand instances offer maximum flexibility but at a higher hourly rate. Reserved instances provide significant discounts (up to 30–40%) for longer-term commitments. Choose on-demand for variable or short-term workloads, and reserved for predictable, ongoing projects. Some providers like Hyperstack offer both options, allowing you to optimize based on your specific needs.

### **What storage options are available, and how do they impact performance?**

H100 SXM rentals often come with high-performance storage options like NVMe SSDs to match the GPU's capabilities. Some providers offer tiered storage solutions, allowing you to balance cost and performance. For optimal performance, especially in distributed training scenarios, ensure your provider offers storage solutions with throughput matching the H100's data processing capabilities.

### **What are the networking capabilities and limitations?**

H100 SXM configurations typically support high-bandwidth networking, often up to 350 Gbps or more. This is crucial for multi-GPU setups and distributed training. Verify that your provider's networking infrastructure can fully support the H100's capabilities, especially if you're planning to run multi-node workloads.

### **How compatible are H100 SXMs with common AI frameworks and software?**

H100 SXMs are highly compatible with popular AI frameworks like PyTorch and TensorFlow. Many providers bundle the NVIDIA AI Enterprise software suite, which includes optimized versions of these frameworks. Always ensure you're using the latest versions of your preferred frameworks to take full advantage of the H100's features, such as FP8 precision and the Transformer Engine.

### **What multi-GPU configuration options are available?**

Providers typically offer various multi-GPU configurations, from single nodes with multiple H100 SXMs to multi-node clusters. The SXM form factor enables high-speed GPU-to-GPU communication via NVLink, which is crucial for scaling performance in distributed training scenarios. Check with your provider for specific multi-GPU options and their associated pricing.

### **How many H100 SXM GPUs do I need for my workload?**

The number of GPUs required depends on your specific use case: For large language model training (70B+ parameters), 8 or more GPUs are often recommended. For smaller models or fine-tuning tasks, 1–4 GPUs may suffice. Real-time inference workloads can often be handled by a single GPU, leveraging the H100's MIG technology to serve multiple models concurrently. Always benchmark your specific workload to determine the optimal configuration.

### **What RunPod-specific features or limitations should I be aware of?**

RunPod is one of the leading [serverless GPU platforms](https://www.runpod.io/articles/top-serverless-gpu-clouds), offering flexible GPU rental options, including H100 SXMs. Specific features or limitations may vary. Check RunPod's documentation for details on available regions and data centers, supported frameworks and software environments, persistent storage options, networking configurations, and support for custom containers or environments. Additionally, consider RunPod's pricing structure, including any discounts for sustained usage or reserved instances, to optimize your costs.

### **How can I monitor usage and manage costs effectively?**

Most providers, including RunPod, offer detailed monitoring and billing dashboards. Best practices include setting up alerts for usage thresholds, regularly reviewing utilization metrics to right-size your resources, leveraging auto-scaling features for dynamic workloads, considering reserved instances for long-term, predictable usage, and using spot instances for fault-tolerant, interruptible workloads to save costs. By staying informed about your usage patterns and leveraging the provider's cost management tools, you can optimize your H100 SXM rental expenses while maximizing performance for your AI workloads.

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