NVIDIA B100 VS NVIDIA H200
Choosing between **B100** and **H200** 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 **$1.49/h** respectively across 4 providers.
📊 Detailed Specifications Comparison
| Specification | B100 | H200 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Hopper | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | SXM5 | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 141GB | +36% |
| Memory Type | HBM3e | HBM3e | - |
| Memory Bandwidth | 8.0 TB/s | 4.8 TB/s | +67% |
| Memory Bus Width | 8192-bit | 6144-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 141GB.
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 H200
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: B100 vs H200
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Hopper. The B100 has a significant **51GB 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 H200 is Best For:
- LLM inference at scale
- Large context window models
- Budget deployments
Frequently Asked Questions
Which GPU is better for AI training: B100 or H200?
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 H200 provides 141GB of HBM3e with 4.8 TB/s bandwidth. For larger models, the B100's higher VRAM capacity gives it an advantage.
What is the price difference between B100 and H200 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 H200 instead of B100 for my workload?
It depends on your specific requirements. If your model fits within 141GB of VRAM and you don't need the additional throughput of the B100, the H200 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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