NVIDIA A100 40GB VS NVIDIA A30
Choosing between **A100 40GB** and **A30** depends on your specific AI workload requirements. The **A100 40GB** 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 **$0.11/h** respectively across 6 providers.
A100 40GB
📊 Detailed Specifications Comparison
| Specification | A100 40GB | A30 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ampere | - |
| Process Node | 7nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM4 / PCIe | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 40GB | 24GB | +67% |
| Memory Type | HBM2 | HBM2 | - |
| Memory Bandwidth | 1.5 TB/s | 933 GB/s | +67% |
| Memory Bus Width | 5120-bit | 3072-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 6,912 | 3,584 | +93% |
| Tensor Cores (AI) | 432 | 224 | +93% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 19.5 TFLOPS | 5.2 TFLOPS | +275% |
| FP16 (Half Precision) | 312 TFLOPS | 165 TFLOPS | +89% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 250W | 165W | +52% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A100 40GB
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A100 40GB offers 40GB compared to 24GB.
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 A30
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: A100 40GB vs A30
Both GPUs utilize the NVIDIA Ampere architecture. The primary difference lies in their memory capacity and compute core counts. The A100 40GB has a significant **16GB 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 A30 is Best For:
- Enterprise AI inference
- Mainstream compute
- Heavy model training
Frequently Asked Questions
Which GPU is better for AI training: A100 40GB or A30?
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 A30 provides 24GB of HBM2 with 933 GB/s bandwidth. For larger models, the A100 40GB's higher VRAM capacity gives it an advantage.
What is the price difference between A100 40GB and A30 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 A30 instead of A100 40GB 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 40GB, the A30 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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