NVIDIA A30 VS NVIDIA A100 80GB
Choosing between **A30** and **A100 80GB** depends on your specific AI workload requirements. The **A100 80GB** 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 **$0.11/h** and **$0.40/h** respectively across 47 providers.
A100 80GB
📊 Detailed Specifications Comparison
| Specification | A30 | A100 80GB | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ampere | - |
| Process Node | 7nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 24GB | 80GB | -70% |
| Memory Type | HBM2 | HBM2e | - |
| Memory Bandwidth | 933 GB/s | 2.0 TB/s | -54% |
| Memory Bus Width | 3072-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 3,584 | 6,912 | -48% |
| Tensor Cores (AI) | 224 | 432 | -48% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 5.2 TFLOPS | 19.5 TFLOPS | -73% |
| FP16 (Half Precision) | 165 TFLOPS | 312 TFLOPS | -47% |
| TF32 (Tensor Float) | N/A | 156 TFLOPS | |
| FP64 (Double Precision) | N/A | 9.7 TFLOPS | |
| INT8 (Integer Precision) | N/A | 624 TOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 165W | 400W | -59% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | None | NVLink 3.0 (600 GB/s) | - |
🎯 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 A30
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A30
Based on current cloud pricing, the A30 starts at a lower hourly rate.
Technical Deep Dive: A30 vs A100 80GB
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 **A30** is currently about **73% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA A30 is Best For:
- Enterprise AI inference
- Mainstream compute
- Heavy model training
NVIDIA A100 80GB is Best For:
- AI model training
- Scientific computing
- Newest FP8 precision workloads
Frequently Asked Questions
Which GPU is better for AI training: A30 or A100 80GB?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The A30 offers 24GB of HBM2 memory with 933 GB/s bandwidth, while the A100 80GB provides 80GB of HBM2e with 2.0 TB/s bandwidth. For larger models, the A100 80GB's higher VRAM capacity gives it an advantage.
What is the price difference between A30 and A100 80GB in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, A30 starts at $0.11/hour while A100 80GB starts at $0.40/hour. This represents a 73% price difference.
Can I use A100 80GB instead of A30 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 A30, the A100 80GB can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the A30's architecture may be essential.
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