NVIDIA H100 SXM VS NVIDIA A800 80GB
Choosing between **H100 SXM** and **A800** depends on your specific AI workload requirements. Currently, you can rent these GPUs starting from **$0.73/h** and **$0.80/h** respectively across 49 providers.
📊 Detailed Specifications Comparison
| Specification | H100 SXM | A800 | 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 | +73% |
| 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 | 400W | +75% |
| 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 A800 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 H100 SXM
Based on current cloud pricing, the H100 SXM starts at a lower hourly rate.
Technical Deep Dive: H100 SXM vs A800
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ampere. From a cost perspective, the **H100 SXM** is currently about **9% 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 A800 80GB is Best For:
- AI training
- Scientific computing
- International high-bandwidth needs
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or A800?
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 A800 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 A800 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while A800 starts at $0.80/hour. This represents a 9% price difference.
Can I use A800 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 A800 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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