NVIDIA H100 SXM VS NVIDIA T4G
Choosing between **H100 SXM** and **T4G** depends on your specific AI workload requirements. The **H100 SXM** 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 **$0.73/h** and **$0.23/h** respectively across 47 providers.
📊 Detailed Specifications Comparison
| Specification | H100 SXM | T4G | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Turing | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | AWS Instance | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 16GB | +400% |
| Memory Type | HBM3 | GDDR6 | - |
| Memory Bandwidth | 3.35 TB/s | 320 GB/s | +947% |
| Memory Bus Width | 5120-bit | 256-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 2,560 | +560% |
| Tensor Cores (AI) | 528 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 8.1 TFLOPS | +727% |
| FP16 (Half Precision) | 1,979 TFLOPS | N/A | |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 70W | +900% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 3.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 SXM
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H100 SXM offers 80GB compared to 16GB.
AI Inference
NVIDIA H100 SXM
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: H100 SXM vs T4G
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Turing. The H100 SXM has a significant **64GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **T4G** is currently about **68% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
NVIDIA T4G is Best For:
- ARM-based AI inference
- x86 native workloads
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or T4G?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 SXM offers 80GB of HBM3 memory with 3.35 TB/s bandwidth, while the T4G provides 16GB of GDDR6 with 320 GB/s bandwidth. For larger models, the H100 SXM's higher VRAM capacity gives it an advantage.
What is the price difference between H100 SXM and T4G in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while T4G starts at $0.23/hour. This represents a 217% price difference.
Can I use T4G instead of H100 SXM 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 H100 SXM, the T4G can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 SXM's NVLink support (NVLink 4.0 (900 GB/s)) may be essential.
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