NVIDIA HGX H100 GPU Rental
Unlock enterprise AI training and inference performance
A mature compute solution built on the NVIDIA Hopper architecture
A leading choice for enterprise AI computing: H100
Built on the NVIDIA Hopper architecture, H100 is a core compute platform for mainstream large-model training and high-performance inference.
From tens-of-billions-parameter model training and distributed fine-tuning to high-concurrency inference services, H100 provides a stable, mature GPU environment with broad ecosystem support.
Choose the right configuration
Flexible deployment options for different business requirements
H100 cloud server
- Single- or multi-GPU instances
- Launch in minutes
- On-demand, monthly, or annual billing
- Suitable for elastic scaling and test environments
H100 8-GPU cluster
- 8× H100 SXM
- High-speed NVLink interconnect
- 3.2 TB/s GPU interconnect bandwidth
- 25 Gbps private network
- 10 Gbps+ public network
- See the specifications below for bare-metal configurations
Data centers: North America and Europe
Delivery: bare metal / virtualized
Which workloads are suited to H100?
Why choose us?
Professional service and reliable support
Cost savings
- Save up to 30%–70%
- Transparent billing with no hidden network fees
Elastic scaling
- Short-term elastic scaling
- Kubernetes support
Enterprise services
- Products compliant with HIPAA and SOC 2
- Backed by an enterprise SLA and a trusted 24/7 support team to keep your services online
- Architecture-level deployment guidance
- Dedicated technical support
H100 GPU Server Specifications
Multiple configurations for AI training and inference at different scales
| Specification | H100 8-GPU bare metal | H100 8-GPU cloud server | H100 single-GPU cloud server |
|---|---|---|---|
| GPU | NVIDIA HGX H100 80GB 700W SXM5 GPUs, fully interconnected with NVIDIA NVLink technology | NVIDIA H100 SXM5 GPUs*8, 80GB* 8 640GB HBM3 Memory | NVIDIA H100 SXM5 GPU 80GB HBM3 Memory |
| CPU | 96 cores 192 Threads Intel(R) Xeon(R) Platinum 8468 *2, 4th Gen Intel® Xeon® Scalable Processors | 160 VCPU | 20 VCPU |
| Memory | 2048GB（64GB*32） | 1920GB | 240GB |
| Local storage | 7TB 2.5-inch NVMe SSD drives*8 | 2TB boot disk，40TB NVMe SSD local disk | 720GB boot disk，5TB NvMe SSD local disk |
| GPU interconnect | Mellanox Network Adapter MT2910 Family ConnectX-7, 400Gbps*8 NVLINK supported RoCE2 3.2Tbs(400Gbps*8) RoCE2 | NVLINK supported，RoCE2 3.2Tbs(400Gbps*8) RDMA network | - |
| Ethernet | Mellanox Technologies MT2892 Family [ConnectX-6 Dx] ; link speed 100Gps*4 | - | - |
| Private network | Up to 400 Gbps | 25Gbps | 25Gbps |
| Public network | Up to 40 Gbps | 10Gbps | 10Gbps |
| Included outbound transfer | Unlimited transfer | 60TB | 15TB |
| Billing model | Annual or monthly | Annual, monthly, or on-demand | Annual, monthly, or on-demand |
* Specifications are subject to the delivered configuration.
NVIDIA HGX H100 Technical specifications
| H100 SXM | Specification |
|---|---|
| FP64 | 34 teraFLOPS |
| FP64 Tensor Core | 67 teraFLOPS |
| FP32 | 67 teraFLOPS |
| TF32 Tensor Core* | 989 teraFLOPS |
| BFLOAT16 Tensor Core* | 1,979 teraFLOPS |
| FP16 Tensor Core* | 1,979 teraFLOPS |
| FP8 Tensor Core* | 3,958 teraFLOPS |
| INT8 Tensor Core* | 3,958 TOPS |
| GPU Memory | 80GB |
| GPU Memory Bandwidth | 3.35TB/s |
| Decoders | 7 NVDEC 7 JPEG |
| Max Thermal Design Power (TDP) | Up to 700W (configurable) |
| Multi-Instance GPUs | Up to 7 MIGS @ 10GB each |
| Form Factor | SXM |
| Interconnect | NVIDIA NVLink™: 900GB/s PCIe Gen5: 128GB/s |
| Server Options | NVIDIA HGX H100 Partner and NVIDIA- Certified Systems™ with 4 or 8 GPUs NVIDIA DGX H100 with 8 GPUs |
| NVIDIA AI Enterprise | Add-on |
Performance Comparison
Comprehensive comparison of technical specifications and performance metrics across different GPU models
| GPU Model | GPU Memory | Memory | vCPU | Boot Disk | Scratch Disk | Architecture |
|---|---|---|---|---|---|---|
| AMD Instinct™ MI325X* | 256 GB | 164 GiB | 20 | 720 GiB NVMe | 5 TiB NVMe | CDNA 3™ |
| AMD Instinct™ MI325X×8* | 2,048 GB | 1,310 GiB | 160 | 2,046 GiB NVMe | 40 TiB NVMe | CDNA 3™ |
| AMD Instinct™ MI300X | 192 GB | 240 GiB | 20 | 720 GiB NVMe | 5 TiB NVMe | CDNA 3™ |
| AMD Instinct™ MI300X×8 | 1,536 GB | 1,920 GiB | 160 | 2,046 GiB NVMe | 40 TiB NVMe | CDNA 3™ |
| NVIDIA H200 | 141 GB | 240 GiB | 24 | 720 GiB NVMe | 5 TiB NVMe | Hopper |
| NVIDIA H200×8 | 1,128 GB | 1,920 GiB | 192 | 2,046 GiB NVMe | 40 TiB NVMe | Hopper |
| NVIDIA H100 | 80 GB | 240 GiB | 20 | 720 GiB NVMe | 5 TiB NVMe | Hopper |
| NVIDIA H100×8 | 640 GB | 1,920 GiB | 160 | 2,046 GiB NVMe | 40 TiB NVMe | Hopper |
| NVIDIA RTX 4000 Ada Generation | 20 GB | 32 GiB | 8 | 500 GiB NVMe | - | Ada Lovelace |
| NVIDIA RTX 6000 Ada Generation | 48 GB | 64 GiB | 8 | 500 GiB NVMe | - | Ada Lovelace |
| NVIDIA L40S | 48 GB | 64 GiB | 8 | 500 GiB NVMe | - | Ada Lovelace |
H100 GPU Rental Guide
H100 Rental, H100 Servers, and AI Compute Selection
H100 remains one of the most mature GPU choices for training, fine-tuning, and inference. This guide covers pricing factors, configuration decisions, upgrade paths to H200 or B300, and related tutorials.
Pricing and configuration considerations
H100 rental pricing depends on GPU count, bare-metal or cloud delivery, rental term, network, storage, and dedicated-capacity requirements. For teams deploying an AI application for the first time, H100 is typically a mature, lower-risk high-performance starting point.
Use cases
- Training or fine-tuning open-source large models with stable GPU capacity.
- AI teams that want to reduce deployment and debugging risk with a mature ecosystem.
- Migrating from consumer graphics cards to production-grade cloud GPUs.
- Enterprise projects seeking a reliable balance between cost and performance.
GPU and cloud server comparison
H100
Mature AI compute baseline
Fits most training, fine-tuning, and inference workloads, with a mature ecosystem and clear delivery options.
H200
Larger memory requirements
H200 has an advantage when context length, concurrency, or model size increases memory pressure.
L40S
Vision and lightweight inference
Fits budget-sensitive vision, multimedia, and small-to-medium model inference workloads.
Frequently asked questions
When is renting H100 better than buying a server?
Renting an H100 cloud server is more flexible when project duration is uncertain, launch timelines are tight, elastic scaling is required, or hardware operations are undesirable.
Can H100 still support new models?
H100 still supports most training, fine-tuning, and inference workloads. Evaluate H200 or B300 when context, memory, or throughput requirements are higher.
Is H100 suitable for DeepSeek or Qwen deployments?
Yes, especially for fine-tuning, inference, and medium-concurrency production workloads. Estimate GPU count from model size, quantization method, and concurrency target.
Related reading and procurement guides
Need a large-scale H100 cluster?
Supports 16-GPU, 32-GPU, and multi-node scaling
Supports long-term enterprise compute reservations
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