NVIDIA T4G VS NVIDIA A30
Choosing between **T4G** and **A30** depends on your specific AI workload requirements. While the **A30** offers more VRAM for larger models, the **T4G** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.23/h** and **$0.11/h** respectively across 7 providers.
📊 Detailed Specifications Comparison
| Specification | T4G | A30 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Turing | Ampere | - |
| Process Node | 12nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | AWS Instance | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 16GB | 24GB | -33% |
| Memory Type | GDDR6 | HBM2 | - |
| Memory Bandwidth | 320 GB/s | 933 GB/s | -66% |
| Memory Bus Width | 256-bit | 3072-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 2,560 | 3,584 | -29% |
| Tensor Cores (AI) | N/A | 224 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 8.1 TFLOPS | 5.2 TFLOPS | +56% |
| FP16 (Half Precision) | N/A | 165 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 70W | 165W | -58% |
| PCIe Interface | PCIe 3.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A30
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A30 offers 24GB compared to 16GB.
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: T4G vs A30
This is a generational comparison within the NVIDIA ecosystem, pitting Turing against Ampere. The A30 has a significant **8GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **A30** is currently about **52% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA T4G is Best For:
- ARM-based AI inference
- x86 native workloads
NVIDIA A30 is Best For:
- Enterprise AI inference
- Mainstream compute
- Heavy model training
Frequently Asked Questions
Which GPU is better for AI training: T4G or A30?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The T4G offers 16GB of GDDR6 memory with 320 GB/s bandwidth, while the A30 provides 24GB of HBM2 with 933 GB/s bandwidth. For larger models, the A30's higher VRAM capacity gives it an advantage.
What is the price difference between T4G and A30 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, T4G starts at $0.23/hour while A30 starts at $0.11/hour. This represents a 109% price difference.
Can I use A30 instead of T4G 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 T4G, the A30 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the T4G's architecture may be essential.
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