NVIDIA B100 VS NVIDIA H100 SXM
Choosing between **B100** and **H100 SXM** depends on your specific AI workload requirements. The **B100** 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 **$0.73/h** respectively across 46 providers.
📊 Detailed Specifications Comparison
| Specification | B100 | H100 SXM | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Hopper | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | SXM5 | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 80GB | +140% |
| Memory Type | HBM3e | HBM3 | - |
| Memory Bandwidth | 8.0 TB/s | 3.35 TB/s | +139% |
| Memory Bus Width | 8192-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,336 | 16,896 | -15% |
| Tensor Cores (AI) | 448 | 528 | -15% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 70 TFLOPS | 67 TFLOPS | +4% |
| FP16 (Half Precision) | 3,500 TFLOPS | 1,979 TFLOPS | +77% |
| TF32 (Tensor Float) | 1,750 TFLOPS | 989 TFLOPS | +77% |
| FP64 (Double Precision) | 35 TFLOPS | 34 TFLOPS | +3% |
| INT8 (Integer Precision) | 7,000 TOPS | 3,958 TOPS | +77% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 700W | |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | None | NVLink 4.0 (900 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA B100
Higher VRAM capacity and memory bandwidth are critical for training large language models. The B100 offers 192GB compared to 80GB.
AI Inference
NVIDIA B100
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA H100 SXM
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: B100 vs H100 SXM
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Hopper. The B100 has a significant **112GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA B100 is Best For:
- Large-scale AI training
- Budget deployments
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
Frequently Asked Questions
Which GPU is better for AI training: B100 or H100 SXM?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The B100 offers 192GB of HBM3e memory with 8.0 TB/s bandwidth, while the H100 SXM provides 80GB of HBM3 with 3.35 TB/s bandwidth. For larger models, the B100's higher VRAM capacity gives it an advantage.
What is the price difference between B100 and H100 SXM 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 H100 SXM instead of B100 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 B100, the H100 SXM can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the B100's architecture may be essential.
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