Specifications Compared
| Spec | B200 | H100 |
|---|---|---|
| TDP | 1000W | 700W |
| VRAM | 192 GB | 80-94 GB |
| CUDA Cores | 18,432 | 16,896 |
| Memory Type | HBM3e | HBM3 |
| Architecture | Blackwell | Hopper |
| Form Factors | SXM, NVL | SXM5, PCIe, NVL |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | NVLink, PCIe 5.0, InfiniBand |
| Tensor Cores | 576 | 528 |
| FP8 Performance | 9,000 TFLOPS | 3,958 TFLOPS |
| FP16 Performance | 4,500 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 90 TFLOPS | 67 TFLOPS |
| FP64 Performance | 45 TFLOPS | 34 TFLOPS |
| INT8 Performance | 9,000 TOPS | 3,958 TOPS |
| Memory Bandwidth | 8,000 GB/s | 3,350 GB/s |
Performance Analysis
The B200 SXM's FP16 performance of 4500 TFLOPS exceeds the H100 SXM5's 1979 TFLOPS by more than 2.2 times, accelerating deep learning training where tensor operations dominate. FP32 throughput at 90 TFLOPS on the B200 SXM improves over the H100 SXM5's 67 TFLOPS, benefiting simulations and general-purpose computing that rely on single-precision arithmetic. FP8 at 9000 TFLOPS on the B200 SXM nearly doubles the H100 SXM5's 3958 TFLOPS, optimizing large language model inference with quantized models.
Memory capacity of 192 GB HBM3e on the B200 SXM supports batch sizes and model sizes unattainable on the H100 SXM5's 80 to 94 GB HBM3, reducing the need for model parallelism. Bandwidth of 8000 GB/s on the B200 SXM versus 3350 GB/s on the H100 SXM5 minimizes data transfer bottlenecks, allowing larger effective batch sizes in training and faster inference throughput. The B200 SXM's 1000W TDP demands more power than the H100 SXM5's 700W, potentially increasing operational costs in dense deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B200 SXM
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Nebius | NVIDIA B200 SXM 192GB VRAM | 192GB | 20 vCPU 224GB RAM | 🌍Europe | $3.95/GPU/hr | |||
 Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $4.79/GPU/hr $38.32/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.39/GPU/hr $43.12/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.69/GPU/hr $45.52/hr total (8×) | |||
 RunPod | NVIDIA B200 SXM 192GB VRAM | 192GB | 28 vCPU 283GB RAM | California | $5.89/GPU/hr |
H100 SXM5
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Hyperstack | 4×NVIDIA H100 PCIe 80GB VRAM | 80GB | 124 vCPU 720GB RAM 3300GB Storage | Canada | $1.90/GPU/hr $7.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA H100 PCIe 80GB VRAM | 80GB | 60 vCPU 360GB RAM 1600GB Storage | Canada | $1.90/GPU/hr $3.80/hr total (2×) | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.90/GPU/hr $15.20/hr total (8×) | Available | ||
Hyperstack | NVIDIA H100 PCIe 80GB VRAM | 80GB | 28 vCPU 180GB RAM 850GB Storage | Canada | $1.90/GPU/hr | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.95/GPU/hr $15.60/hr total (8×) | Available |
When to Choose the B200 SXM
The B200 SXM excels in scenarios demanding maximum memory capacity and compute density, such as training massive language models exceeding 100 billion parameters that require 192 GB HBM3e VRAM. Its 8000 GB/s bandwidth and 4500 TFLOPS FP16 performance enable faster iterations on large datasets. Deploy it when future-proofing infrastructure for next-generation AI workloads justifies the $1.71 per hour starting price.
When to Choose the H100 SXM5
The H100 SXM5 suits budget-conscious deployments with its lower entry price of $0.80 per hour and wider availability across 36 cloud offers. It handles most current AI tasks effectively with 1979 TFLOPS FP16 and 3350 GB/s bandwidth, while its 700W TDP reduces power expenses. Choose it for mature workflows where ecosystem support outweighs the B200 SXM's raw advantages.
Use Cases
The B200 SXM's 4500 TFLOPS FP16 and 192 GB HBM3e VRAM support training of models over 100 billion parameters without excessive sharding. Its 8000 GB/s bandwidth handles massive datasets more efficiently than the H100 SXM5.
FP8 performance of 9000 TFLOPS on the B200 SXM doubles the H100 SXM5's 3958 TFLOPS, boosting quantized inference throughput. Larger 192 GB VRAM accommodates bigger batches for high-query-volume services.
Fine-tuning mid-sized models fits within the H100 SXM5's 80 to 94 GB VRAM and 1979 TFLOPS FP16. The B200 SXM offers speedup for larger adaptations via 4500 TFLOPS FP16.
The B200 SXM's 192 GB VRAM and 8000 GB/s bandwidth enable high-resolution image generation at scale. FP16 at 4500 TFLOPS accelerates diffusion steps far beyond the H100 SXM5.
The H100 SXM5's 67 TFLOPS FP32 suffices for many simulations at lower cost of $0.80 per hour. Its mature software stack integrates seamlessly with scientific libraries.
Frequently Asked Questions
Which GPU has more VRAM: B200 SXM or H100 SXM5?▾
The B200 SXM provides 192 GB HBM3e VRAM, doubling the H100 SXM5's maximum of 94 GB HBM3. This allows the B200 SXM to load larger models without partitioning. H100 SXM5 remains viable for models under 70 billion parameters.
How do cloud prices compare for B200 SXM and H100 SXM5?▾
B200 SXM starts at $1.71 per hour with an average of $4.60 per hour across 13 offers. H100 SXM5 is cheaper at $0.80 per hour average $3.42 per hour over 36 offers. Availability favors H100 SXM5 for immediate scaling.
Which is faster for FP16 workloads?▾
B200 SXM delivers 4500 TFLOPS FP16, more than 2.2 times the H100 SXM5's 1979 TFLOPS. This translates to quicker AI training cycles. Bandwidth of 8000 GB/s on B200 SXM further amplifies real-world gains.
What are the power requirements?▾
B200 SXM has a 1000W TDP, higher than the H100 SXM5's 700W. This increases cooling and electricity costs for B200 SXM deployments. H100 SXM5 fits better in power-constrained environments.
Which supports faster interconnects?▾
B200 SXM includes PCIe 6.0 alongside NVLink and InfiniBand, advancing beyond H100 SXM5's PCIe 5.0. This enables higher multi-GPU bandwidth in clusters. Both share NVLink for tight scaling.
Is B200 SXM worth the premium over H100 SXM5?▾
B200 SXM justifies its $1.71 per hour start for workloads leveraging 9000 TFLOPS FP8 or 192 GB VRAM. H100 SXM5 at $0.80 per hour suits cost-sensitive tasks with 3958 TFLOPS FP8. Choice depends on model scale and performance needs.
Which is cheaper to rent, the B200 or the H100?▾
Cloud rental prices for both the B200 and H100 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the B200 have compared to the H100?▾
The B200 has 192 GB of HBM3e memory. The H100 has 80 to 94 GB of HBM3 memory.
Can I find B200 and H100 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the B200 and the H100?▾
The B200 uses the Blackwell architecture (2024) while the H100 uses Hopper (2022). The B200 delivers 2.3x the FP16 throughput and 2.4x the memory bandwidth of the H100.