NVIDIA H100 PCIe VS NVIDIA A100 40GB
Choosing between **H100 PCIe** and **A100 40GB** depends on your specific AI workload requirements. The **H100 PCIe** 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 **$0.00/h** and **$0.00/h** respectively across 0 providers.
H100 PCIe
A100 40GB
📊 Detailed Specifications Comparison
| Specification | H100 PCIe | A100 40GB | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ampere | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 40GB | +100% |
| Memory Type | HBM3 | HBM2 | - |
| Memory Bandwidth | 2.0 TB/s | 1.5 TB/s | +29% |
| Memory Bus Width | 5120-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,592 | 6,912 | +111% |
| Tensor Cores (AI) | 456 | 432 | +6% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 51 TFLOPS | 19.5 TFLOPS | +162% |
| FP16 (Half Precision) | 1,513 TFLOPS | 312 TFLOPS | +385% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 350W | 250W | +40% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 PCIe
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H100 PCIe offers 80GB compared to 40GB.
AI Inference
NVIDIA H100 PCIe
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A100 40GB
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: H100 PCIe vs A100 40GB
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ampere. The H100 PCIe has a significant **40GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA H100 PCIe is Best For:
- AI inference
- Enterprise AI
- Highest-end training
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 PCIe or A100 40GB?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 PCIe offers 80GB of HBM3 memory with 2.0 TB/s bandwidth, while the A100 40GB provides 40GB of HBM2 with 1.5 TB/s bandwidth. For larger models, the H100 PCIe's higher VRAM capacity gives it an advantage.
What is the price difference between H100 PCIe 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 PCIe 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 PCIe, the A100 40GB can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 PCIe's architecture may be essential.
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