NVIDIA H100 SXM VS NVIDIA H200
Choosing between **H100 SXM** and **H200** depends on your specific AI workload requirements. While the **H200** offers more VRAM for larger models, the **H100 SXM** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.73/h** and **$1.49/h** respectively across 50 providers.
📊 Detailed Specifications Comparison
| Specification | H100 SXM | H200 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Hopper | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | SXM5 | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 141GB | -43% |
| Memory Type | HBM3 | HBM3e | - |
| Memory Bandwidth | 3.35 TB/s | 4.8 TB/s | -30% |
| Memory Bus Width | 5120-bit | 6144-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 16,896 | |
| Tensor Cores (AI) | 528 | 528 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 67 TFLOPS | |
| FP16 (Half Precision) | 1,979 TFLOPS | 1,979 TFLOPS | |
| TF32 (Tensor Float) | 989 TFLOPS | 989 TFLOPS | |
| FP64 (Double Precision) | 34 TFLOPS | 34 TFLOPS | |
| INT8 (Integer Precision) | 3,958 TOPS | 3,958 TOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 700W | |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | NVLink 4.0 (900 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H200
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H200 offers 141GB compared to 80GB.
AI Inference
NVIDIA H200
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA H100 SXM
Based on current cloud pricing, the H100 SXM starts at a lower hourly rate.
Technical Deep Dive: H100 SXM vs H200
Both GPUs utilize the NVIDIA Hopper architecture. The primary difference lies in their memory capacity and compute core counts. The H200 has a significant **61GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **H100 SXM** is currently about **51% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
NVIDIA H200 is Best For:
- LLM inference at scale
- Large context window models
- Budget deployments
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or H200?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 SXM offers 80GB of HBM3 memory with 3.35 TB/s bandwidth, while the H200 provides 141GB of HBM3e with 4.8 TB/s bandwidth. For larger models, the H200's higher VRAM capacity gives it an advantage.
What is the price difference between H100 SXM and H200 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while H200 starts at $1.49/hour. This represents a 51% price difference.
Can I use H200 instead of H100 SXM for my workload?
It depends on your specific requirements. If your model fits within 141GB of VRAM and you don't need the additional throughput of the H100 SXM, the H200 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 SXM's NVLink support (NVLink 4.0 (900 GB/s)) may be essential.
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