Rent H100 NVL in the Cloud – Deploy in Seconds on RunPod
Instant access to NVIDIA H100 NVL GPUs—ideal for large language models and generative AI—with hourly pricing, global availability, and fast deployment through RunPod. Harness cutting-edge performance with fourth-generation Tensor Cores and NVLink technology for seamless scalability and dramatic AI training speedups. Experience cost-effective, flexible GPU power without the capital expenditure and maintenance overhead.
Why Choose NVIDIA H100 NVL
The NVIDIA H100 NVL GPU offers unparalleled performance, significant cost benefits, and flexible deployment options, making it an ideal choice for organizations tackling demanding AI and high-performance computing workloads.
- Unparalleled Performance
The H100's specialized Transformer Engine revolutionizes AI computing, delivering up to 30X higher inference performance compared to previous generations. This acceleration is particularly noticeable when deploying and running popular LLM models and generative AI tasks. - Cost Benefits
Renting H100s transforms the financial equation by converting capital expenditure to operational expense, allowing organizations to pay only for actual usage and eliminating maintenance costs and depreciation concerns. This makes it a cost-effective solution for deploying AI models. - Flexibility and Scalability
Renting enables organizations to scale resources based on project requirements, test different configurations without long-term commitments, and handle burst computing needs without overprovisioning, including deploying models via custom API endpoints for hosting models. - Industry Compatibility
The H100 integrates seamlessly with popular AI frameworks like PyTorch and TensorFlow, supporting a wide range of industries from scientific simulations and real-time data analytics to complex rendering and advanced research.
Specifications
| Feature | Value |
|---|---|
| GPU Architecture | NVIDIA Hopper (GH100) |
| CUDA Cores | 14,592 |
| Tensor Cores | 456 (PCIe variant) / 640 (SXM variant) |
| GPU Memory | 80 GB HBM3 |
| Memory Bandwidth | Up to 3.35 TB/s (SXM); 2.04 TB/s (PCIe) |
| Memory Bus | 5,120-bit |
| L2 Cache | 50 MB |
| Base Clock | 1,095 MHz |
| Boost Clock | Up to 1,755 MHz |
| Thermal Design Power (TDP) | 350W (PCIe) / up to 700W (SXM) |
| Process Technology | 4nm |
| FP64 Performance | 34 TFLOPS (SXM) / 26 TFLOPS (PCIe) |
| FP64 Tensor Core Performance | 67 TFLOPS (SXM) / 51 TFLOPS (PCIe) |
| FP32 Performance | 67 TFLOPS (SXM) / 51 TFLOPS (PCIe) |
| TF32 Tensor Core Performance | 989 TFLOPS (SXM) / 756 TFLOPS (PCIe) |
| BFLOAT16 Tensor Core Performance | 1,979 TFLOPS (SXM) / 1,513 TFLOPS (PCIe) |
| FP16 Tensor Core Performance | 1,979 TFLOPS (SXM) / 1,513 TFLOPS (PCIe) |
| FP8 Tensor Core Performance | 3,958 TFLOPS (SXM) / 3,026 TFLOPS (PCIe) |
| INT8 Tensor Core Performance | 3,958 TOPS (SXM) / 3,026 TOPS (PCIe) |
FAQ
H100 GPU rental prices have dropped dramatically in recent months. While rates hovered around $8 per hour in late 2023, prices have fallen to as low as $1–2 per hour in early 2025. This price reduction stems from improved supply, increased competition among providers, and expanded cloud infrastructure. When budgeting for H100 rentals, consider additional costs such as data transfer fees, storage costs for datasets and model checkpoints, and premium charges for guaranteed availability or dedicated instances. Refer to the pricing structure for GPU instances for detailed information. Some providers offer bundle pricing that includes storage and data transfer allowances, which may prove more cost-effective for data-intensive workloads.
How does the performance of rented H100 GPUs compare to owned hardware?
Performance consistency is a common concern with rented GPUs. On shared infrastructure, you might experience some variability compared to dedicated hardware. Industry analysis shows that performance on shared infrastructure can fluctuate during peak usage times, potentially affecting training and inference speeds. Many providers now offer performance tiers: Shared instances (most affordable but variable performance), Dedicated instances (consistent performance with guaranteed resources), and Premium instances (optimized for specific workloads with additional features). For critical production workloads, dedicated instances typically provide the most reliable performance, though at a higher price point.
What is the current availability of H100 GPUs for rental?
H100 availability has improved significantly: Wait times have decreased from 8–11 months to 3–4 months in many regions. Major cloud providers have substantially increased their H100 inventory, and regional availability has expanded beyond traditional data center hubs. AWS has introduced flexible scheduling options allowing customers to reserve GPUs for specific time periods, addressing previous availability challenges. Some specialty providers now focus on high-availability H100 clusters for enterprise customers, offering guaranteed access with appropriate advance notice.
When does renting H100 GPUs make more sense than purchasing?
Renting H100s is particularly advantageous in scenarios such as short-term projects or proof-of-concept development, workloads with variable computing needs, organizations with limited capital for hardware investments, and teams wanting to avoid technology obsolescence risk. Financial analysis shows that for projects under 9–12 months, renting typically costs less than purchasing when factoring in all expenses (power, cooling, maintenance, infrastructure). For ongoing production workloads with predictable usage patterns, ownership might make more financial sense in the long run—though this calculation continues to shift as rental prices decline.
What security considerations apply when renting H100 GPUs?
Security remains a key concern, especially for sensitive data workloads. Considerations include data protection during transfer and processing, compliance with industry regulations (HIPAA, GDPR, etc.), and limited physical control over hardware in cloud environments. When working with confidential data or proprietary models, evaluate providers based on their encryption capabilities (at-rest and in-transit), compliance certifications, virtual private cloud options, and data deletion guarantees, referring to their security protocols and compliance certifications. Some specialized providers now offer enhanced security features specifically designed for AI workloads involving sensitive data.
How scalable are rented H100 GPU solutions?