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NVIDIA H100 Server
NVIDIA H100 Server is a professional AI/ML GPU cloud server available from Immers Cloud. The H100 is NVIDIA's workhorse data center GPU, widely adopted for AI training and inference across the industry.
Specifications
| Component | Specification |
|---|---|
| GPU | NVIDIA H100 SXM (Hopper architecture) |
| VRAM | 80 GB HBM2e |
| Memory Bandwidth | 3.35 TB/s |
| FP16 Performance | ~989 TFLOPS |
| FP8 Performance | ~1,979 TFLOPS |
| Interconnect | NVLink 4.0 (900 GB/s) |
| Starting Price | From $3.83/hr |
Performance
The H100 is the industry standard for AI/ML workloads in 2024–2026. Key performance characteristics:
- 4th-gen Tensor Cores with FP8 support — 2x throughput vs A100 for training
- 3.35 TB/s memory bandwidth — 2x the A100's bandwidth
- Transformer Engine — hardware acceleration specifically for transformer-based models
- 80 GB HBM2e — sufficient for most production models
Compared to the NVIDIA A100 Server ($2.37/hr):
- 2–3x faster for transformer training (FP8 + Transformer Engine)
- 2x higher memory bandwidth
- Same VRAM capacity (80 GB)
- 62% higher cost per hour, but 40–60% less total cost for training jobs due to speed
Best Use Cases
- AI model training (7B–70B parameter models)
- Large-scale inference serving
- Fine-tuning foundation models (LoRA, QLoRA, full fine-tune)
- Natural language processing research
- Computer vision model training
- Generative AI (text, image, video generation)
- Reinforcement learning from human feedback (RLHF)
Pros and Cons
Advantages
- Industry-standard AI training GPU
- FP8 Tensor Cores for maximum training throughput
- Transformer Engine for transformer model acceleration
- 80 GB VRAM handles most production models
- Excellent software ecosystem (CUDA, cuDNN, TensorRT)
- NVLink 4.0 for efficient multi-GPU training
Limitations
- 80 GB VRAM may be tight for 70B+ models without quantization
- $3.83/hr cost accumulates quickly for long training runs
- High demand can affect availability
- Requires CUDA expertise for optimal utilization
Pricing
Available from Immers Cloud starting at $3.83/hr. For context: training a 7B model fine-tune might take 4–8 hours ($15–30), while training from scratch can cost hundreds to thousands of dollars.
Recommendation
The NVIDIA H100 Server is the default recommendation for serious AI/ML workloads. It offers the best balance of performance, VRAM capacity, and cost for most use cases. Start here if you're training or fine-tuning models in the 7B–70B range. For budget-conscious workloads, consider the NVIDIA A100 Server. For maximum VRAM, upgrade to the NVIDIA H200 Server.