NVIDIA B200 VS NVIDIA H100 SXM
Choosing between **B200** and **H100 SXM** depends on your specific AI workload requirements. The **B200** 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 **$2.25/h** and **$0.73/h** respectively across 66 providers.
📊 Detailed Specifications Comparison
| Specification | B200 | 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 | 18,432 | 16,896 | +9% |
| Tensor Cores (AI) | 576 | 528 | +9% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 90 TFLOPS | 67 TFLOPS | +34% |
| FP16 (Half Precision) | 4,500 TFLOPS | 1,979 TFLOPS | +127% |
| TF32 (Tensor Float) | 2,250 TFLOPS | 989 TFLOPS | +128% |
| FP64 (Double Precision) | 45 TFLOPS | 34 TFLOPS | +32% |
| INT8 (Integer Precision) | 9,000 TOPS | 3,958 TOPS | +127% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1000W | 700W | +43% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | NVLink 5.0 (1.8 TB/s) | NVLink 4.0 (900 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA B200
Higher VRAM capacity and memory bandwidth are critical for training large language models. The B200 offers 192GB compared to 80GB.
AI Inference
NVIDIA B200
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA H100 SXM
Based on current cloud pricing, the H100 SXM starts at a lower hourly rate.
Technical Deep Dive: B200 vs H100 SXM
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Hopper. The B200 has a significant **112GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **H100 SXM** is currently about **68% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA B200 is Best For:
- Next-gen LLM training
- Trillion parameter models
- Cost-sensitive projects
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: B200 or H100 SXM?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The B200 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 B200's higher VRAM capacity gives it an advantage.
What is the price difference between B200 and H100 SXM in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, B200 starts at $2.25/hour while H100 SXM starts at $0.73/hour. This represents a 208% price difference.
Can I use H100 SXM instead of B200 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 B200, the H100 SXM can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the B200's NVLink support (NVLink 5.0 (1.8 TB/s)) may be essential.
Ready to rent a GPU?
Compare live pricing across 50+ cloud providers and find the best deal.