NVIDIA A100 80GB VS NVIDIA H100 SXM
Choosing between **A100 80GB** and **H100 SXM** depends on your specific AI workload requirements. Currently, you can rent these GPUs starting from **$0.40/h** and **$0.73/h** respectively across 87 providers.
A100 80GB
📊 Detailed Specifications Comparison
| Specification | A100 80GB | H100 SXM | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Hopper | - |
| Process Node | 7nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM4 / PCIe | SXM5 | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 80GB | |
| Memory Type | HBM2e | HBM3 | - |
| Memory Bandwidth | 2.0 TB/s | 3.35 TB/s | -39% |
| Memory Bus Width | 5120-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 6,912 | 16,896 | -59% |
| Tensor Cores (AI) | 432 | 528 | -18% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 19.5 TFLOPS | 67 TFLOPS | -71% |
| FP16 (Half Precision) | 312 TFLOPS | 1,979 TFLOPS | -84% |
| TF32 (Tensor Float) | 156 TFLOPS | 989 TFLOPS | -84% |
| FP64 (Double Precision) | 9.7 TFLOPS | 34 TFLOPS | -71% |
| INT8 (Integer Precision) | 624 TOPS | 3,958 TOPS | -84% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 400W | 700W | -43% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | NVLink 3.0 (600 GB/s) | NVLink 4.0 (900 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 H100 SXM 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: A100 80GB vs H100 SXM
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 A100 80GB is Best For:
- AI model training
- Scientific computing
- Newest FP8 precision workloads
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
Frequently Asked Questions
Which GPU is better for AI training: A100 80GB or H100 SXM?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The A100 80GB offers 80GB of HBM2e memory with 2.0 TB/s bandwidth, while the H100 SXM provides 80GB of HBM3 with 3.35 TB/s bandwidth. Both GPUs have similar VRAM capacity, so performance characteristics become the deciding factor.
What is the price difference between A100 80GB and H100 SXM in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, A100 80GB starts at $0.40/hour while H100 SXM starts at $0.73/hour. This represents a 45% price difference.
Can I use H100 SXM instead of A100 80GB 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 A100 80GB, the H100 SXM can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the A100 80GB's NVLink support (NVLink 3.0 (600 GB/s)) may be essential.
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