NVIDIA A100 40GB VS NVIDIA A800 80GB
Choosing between **A100 40GB** and **A800** depends on your specific AI workload requirements. While the **A800** offers more VRAM for larger models, the **A100 40GB** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.00/h** and **$0.80/h** respectively across 3 providers.
A100 40GB
📊 Detailed Specifications Comparison
| Specification | A100 40GB | A800 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ampere | - |
| Process Node | 7nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM4 / PCIe | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 40GB | 80GB | -50% |
| Memory Type | HBM2 | HBM2e | - |
| Memory Bandwidth | 1.5 TB/s | 2.0 TB/s | -20% |
| Memory Bus Width | 5120-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 6,912 | 6,912 | |
| Tensor Cores (AI) | 432 | 432 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 19.5 TFLOPS | 19.5 TFLOPS | |
| FP16 (Half Precision) | 312 TFLOPS | 312 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 250W | 400W | -38% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A800 80GB
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A800 offers 80GB compared to 40GB.
AI Inference
NVIDIA A100 40GB
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A800 80GB
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: A100 40GB vs A800
Both GPUs utilize the NVIDIA Ampere architecture. The primary difference lies in their memory capacity and compute core counts. The A800 has a significant **40GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA A100 40GB is Best For:
- Mainstream AI training
- Scientific computing
- Memory-intensive LLM training
NVIDIA A800 80GB is Best For:
- AI training
- Scientific computing
- International high-bandwidth needs
Frequently Asked Questions
Which GPU is better for AI training: A100 40GB or A800?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The A100 40GB offers 40GB of HBM2 memory with 1.5 TB/s bandwidth, while the A800 provides 80GB of HBM2e with 2.0 TB/s bandwidth. For larger models, the A800's higher VRAM capacity gives it an advantage.
What is the price difference between A100 40GB and A800 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 A800 instead of A100 40GB 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 40GB, the A800 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the A100 40GB's architecture may be essential.
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