NVIDIA H100 SXM VS NVIDIA A100 80GB
Choosing between **H100 SXM** and **A100 80GB** depends on your specific AI workload requirements. Currently, you can rent these GPUs starting from **$0.73/h** and **$0.40/h** respectively across 87 providers.
A100 80GB
📊 Detailed Specifications Comparison
| Specification | H100 SXM | A100 80GB | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ampere | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 80GB | |
| Memory Type | HBM3 | HBM2e | - |
| Memory Bandwidth | 3.35 TB/s | 2.0 TB/s | +64% |
| 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 | 156 TFLOPS | +534% |
| FP64 (Double Precision) | 34 TFLOPS | 9.7 TFLOPS | +251% |
| INT8 (Integer Precision) | 3,958 TOPS | 624 TOPS | +534% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 400W | +75% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | NVLink 3.0 (600 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 SXM
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A100 80GB offers 80GB compared to 80GB.
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 A100 80GB
Based on current cloud pricing, the A100 80GB starts at a lower hourly rate.
Technical Deep Dive: H100 SXM vs A100 80GB
Architectural Leap
The transition from A100 (Ampere) to H100 (Hopper) represents a massive leap in AI performance. The H100 introduces the Transformer Engine, which can automatically manage precision to speed up LLM training by up to 9x. While the A100 remains a workhorse with its 80GB HBM2e memory, the H100’s 80GB HBM3 provides nearly double the bandwidth (3.35 TB/s vs 2.0 TB/s).
Cost Analysis
H100 instances typically rent for $2.00 - $4.50/hr, whereas A100s are now significantly cheaper, often found between $0.80 - $2.00/hr. For legacy workloads or models that don’t utilize FP8, the A100 might offer better value per dollar.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
NVIDIA A100 80GB is Best For:
- AI model training
- Scientific computing
- Newest FP8 precision workloads
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or A100 80GB?
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 80GB provides 80GB of HBM2e with 2.0 TB/s bandwidth. Both GPUs have similar VRAM capacity, so performance characteristics become the deciding factor.
What is the price difference between H100 SXM and A100 80GB in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while A100 80GB starts at $0.40/hour. This represents a 82% price difference.
Can I use A100 80GB instead of H100 SXM for my workload?
It depends on your specific requirements. If your model fits within 80GB of VRAM and you don't need the additional throughput of the H100 SXM, the A100 80GB 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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