NVIDIA GB200 NVL72 VS NVIDIA T4G
Choosing between **GB200** and **T4G** depends on your specific AI workload requirements. The **GB200** 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 **$10.50/h** and **$0.23/h** respectively across 4 providers.
📊 Detailed Specifications Comparison
| Specification | GB200 | T4G | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Turing | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Rack-scale | AWS Instance | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 384GB | 16GB | +2300% |
| Memory Type | HBM3e | GDDR6 | - |
| Memory Bandwidth | 16.0 TB/s | 320 GB/s | +4900% |
| Memory Bus Width | 8192-bit | 256-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 36,864 | 2,560 | +1340% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 180 TFLOPS | 8.1 TFLOPS | +2122% |
| FP16 (Half Precision) | 9,000 TFLOPS | N/A | |
| INT8 (Integer Precision) | 18,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1200W | 70W | +1614% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA GB200 NVL72
Higher VRAM capacity and memory bandwidth are critical for training large language models. The GB200 offers 384GB compared to 16GB.
AI Inference
NVIDIA GB200 NVL72
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA T4G
Based on current cloud pricing, the T4G starts at a lower hourly rate.
Technical Deep Dive: GB200 vs T4G
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Turing. The GB200 has a significant **368GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **T4G** is currently about **98% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA GB200 NVL72 is Best For:
- Massive LLM training
- Trillion-parameter models
- Single-node tasks
NVIDIA T4G is Best For:
- ARM-based AI inference
- x86 native workloads
Frequently Asked Questions
Which GPU is better for AI training: GB200 or T4G?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The GB200 offers 384GB of HBM3e memory with 16.0 TB/s bandwidth, while the T4G provides 16GB of GDDR6 with 320 GB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between GB200 and T4G in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GB200 starts at $10.50/hour while T4G starts at $0.23/hour. This represents a 4465% price difference.
Can I use T4G instead of GB200 for my workload?
It depends on your specific requirements. If your model fits within 16GB of VRAM and you don't need the additional throughput of the GB200, the T4G can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the GB200's architecture may be essential.
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