NVIDIA A100 80GB VS NVIDIA A10
Choosing between **A100 80GB** and **A10** depends on your specific AI workload requirements. While the **A100 80GB** offers more VRAM for larger models, the **A10** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.40/h** and **$0.40/h** respectively across 82 providers.
A100 80GB
📊 Detailed Specifications Comparison
| Specification | A100 80GB | A10 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ampere | - |
| Process Node | 7nm | 8nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM4 / PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 24GB | +233% |
| Memory Type | HBM2e | GDDR6 | - |
| Memory Bandwidth | 2.0 TB/s | 600 GB/s | +240% |
| Memory Bus Width | 5120-bit | 384-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 6,912 | 9,216 | -25% |
| Tensor Cores (AI) | 432 | 288 | +50% |
| RT Cores (Ray Tracing) | N/A | 72 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 19.5 TFLOPS | 31.2 TFLOPS | -38% |
| FP16 (Half Precision) | 312 TFLOPS | 62.4 TFLOPS | +400% |
| TF32 (Tensor Float) | 156 TFLOPS | N/A | |
| FP64 (Double Precision) | 9.7 TFLOPS | N/A | |
| INT8 (Integer Precision) | 624 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 400W | 150W | +167% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 3.0 (600 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A100 80GB
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A100 80GB offers 80GB compared to 24GB.
AI Inference
NVIDIA A100 80GB
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A10
Based on current cloud pricing, the A10 starts at a lower hourly rate.
Technical Deep Dive: A100 80GB vs A10
Both GPUs utilize the NVIDIA Ampere architecture. The primary difference lies in their memory capacity and compute core counts. The A100 80GB has a significant **56GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **A10** is currently about **0% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA A100 80GB is Best For:
- AI model training
- Scientific computing
- Newest FP8 precision workloads
NVIDIA A10 is Best For:
- AI inference
- Cloud gaming
- Heavy LLM training
Frequently Asked Questions
Which GPU is better for AI training: A100 80GB or A10?
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 A10 provides 24GB of GDDR6 with 600 GB/s bandwidth. For larger models, the A100 80GB's higher VRAM capacity gives it an advantage.
What is the price difference between A100 80GB and A10 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, A100 80GB starts at $0.40/hour while A10 starts at $0.40/hour. This represents a 0% price difference.
Can I use A10 instead of A100 80GB for my workload?
It depends on your specific requirements. If your model fits within 24GB of VRAM and you don't need the additional throughput of the A100 80GB, the A10 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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