NVIDIA H100 SXM VS NVIDIA A100 40GB
Choosing between **H100 SXM** and **A100 40GB** depends on your specific AI workload requirements. The **H100 SXM** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$0.73/h** and **$0.00/h** respectively across 46 providers.
A100 40GB
📊 Detailed Specifications Comparison
| Specification | H100 SXM | A100 40GB | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ampere | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 40GB | +100% |
| Memory Type | HBM3 | HBM2 | - |
| Memory Bandwidth | 3.35 TB/s | 1.5 TB/s | +115% |
| Memory Bus Width | 5120-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 6,912 | +144% |
| Tensor Cores (AI) | 528 | 432 | +22% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 19.5 TFLOPS | +244% |
| FP16 (Half Precision) | 1,979 TFLOPS | 312 TFLOPS | +534% |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 250W | +180% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 SXM
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H100 SXM offers 80GB compared to 40GB.
AI Inference
NVIDIA H100 SXM
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA H100 SXM
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: H100 SXM vs A100 40GB
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ampere. The H100 SXM has a significant **40GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
NVIDIA A100 40GB is Best For:
- Mainstream AI training
- Scientific computing
- Memory-intensive LLM training
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or A100 40GB?
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 A100 40GB provides 40GB of HBM2 with 1.5 TB/s bandwidth. For larger models, the H100 SXM's higher VRAM capacity gives it an advantage.
What is the price difference between H100 SXM and A100 40GB in the cloud?
Cloud GPU rental prices vary by provider and region. Check our price tracker for the latest rates from 50+ cloud providers.
Can I use A100 40GB instead of H100 SXM for my workload?
It depends on your specific requirements. If your model fits within 40GB of VRAM and you don't need the additional throughput of the H100 SXM, the A100 40GB 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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