Specifications Compared
| Spec | B200 | V100 |
|---|---|---|
| TDP | 1000W | 300W |
| VRAM | 192 GB | 16-32 GB |
| CUDA Cores | 18,432 | 5,120 |
| Memory Type | HBM3e | HBM2 |
| Architecture | Blackwell | Volta |
| Form Factors | SXM, NVL | SXM2, PCIe |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | NVLink, PCIe 3.0 |
| Tensor Cores | 576 | 640 |
| FP8 Performance | 9,000 TFLOPS | |
| FP16 Performance | 4,500 TFLOPS | 125 TFLOPS |
| FP32 Performance | 90 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 45 TFLOPS | 7.8 TFLOPS |
| INT8 Performance | 9,000 TOPS | |
| Memory Bandwidth | 8,000 GB/s | 900 GB/s |
Performance Analysis
The B200's FP16 performance of 4500 TFLOPS dwarfs the V100's 125 TFLOPS: this enables dramatically faster training of deep learning models, where mixed-precision computations dominate, reducing epoch times by over 30 times in large-scale scenarios. FP32 throughput also improves from 15.7 TFLOPS to 90 TFLOPS, benefiting precision-sensitive inference and simulations. For inference, the B200's FP8 capability at 9000 TFLOPS further accelerates low-precision deployments common in production LLMs.
Memory bandwidth represents a critical bottleneck: the B200's 8000 GB/s versus 900 GB/s allows for much larger batch sizes without out-of-memory errors, supporting models with billions of parameters that the V100 cannot accommodate beyond 32 GB VRAM. In training workflows, this translates to higher throughput and efficiency on datasets like ImageNet or large corpora. The B200's 1000W TDP contrasts with the V100's 300W, demanding robust cooling but delivering proportional power-scaled gains.
Overall, these differences mean the B200 excels in memory-intensive tasks, while the V100 remains viable for smaller batches or FP32-dominant scientific computing.
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 |
Tesla V100 32GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the B200 SXM
Opt for the NVIDIA B200 SXM in scenarios demanding extreme scale: large language model training requires its 192 GB HBM3e VRAM and 4500 TFLOPS FP16 to process models exceeding 100 billion parameters without multi-GPU sharding. High-bandwidth inference benefits from 8000 GB/s and FP8 at 9000 TFLOPS, enabling low-latency serving at scale.
Cloud deployments prioritizing speed over cost favor the B200, especially with NVLink and PCIe 6.0 interconnects for cluster efficiency.
When to Choose the Tesla V100 32GB
Select the NVIDIA Tesla V100 32GB for budget-conscious or legacy applications: its $0.29 per hour starting price suits prototyping, small-scale fine-tuning, or workloads fitting within 32 GB HBM2 and 125 TFLOPS FP16. Lower 300W TDP reduces operational costs in power-sensitive environments.
It remains ideal when compatibility with older Volta-optimized code or PCIe 3.0 setups outweighs raw performance needs.
Use Cases
The B200's 192 GB VRAM and 4500 TFLOPS FP16 handle massive models exceeding the V100's 32 GB limit. Bandwidth at 8000 GB/s supports large batches for efficient training.
FP8 performance of 9000 TFLOPS on the B200 accelerates high-throughput serving. Its VRAM capacity fits full models without quantization compromises needed on V100.
B200's 90 TFLOPS FP32 and high bandwidth enable faster iterations on parameter-efficient methods. V100 struggles with memory for mid-sized models.
B200 processes high-resolution generations rapidly with 4500 TFLOPS FP16. Larger VRAM allows bigger batches than V100's 32 GB.
V100 suffices for FP32 tasks at 15.7 TFLOPS within 32 GB. B200 excels for memory-heavy simulations but at higher cost.
Frequently Asked Questions
What is the VRAM difference between B200 SXM and V100 32GB?▾
The B200 SXM offers 192 GB HBM3e VRAM, six times more than the V100 32GB's 32 GB HBM2. This enables larger models on B200. V100 limits scale in memory-intensive tasks.
How much faster is B200 in FP16 compared to V100?▾
B200 delivers 4500 TFLOPS FP16, 36 times the V100's 125 TFLOPS. Training speeds improve proportionally in mixed precision. Inference also benefits significantly.
What are the cloud rental prices for these GPUs?▾
B200 SXM starts at $1.71 per hour, averaging $4.60 across 13 offers. V100 32GB begins at $0.29 per hour, averaging $1.01 over 46 offers. V100 provides better value for light use.
Does B200 have higher memory bandwidth than V100?▾
B200 achieves 8000 GB/s, nearly nine times the V100's 900 GB/s. Larger batch sizes result on B200. This reduces data loading bottlenecks.
What is the power consumption of B200 vs V100?▾
B200 TDP is 1000W, over three times the V100's 300W. B200 requires advanced cooling. V100 suits lower-power setups.
Can V100 run modern LLMs that B200 handles?▾
V100's 32 GB VRAM limits it to small LLMs with heavy quantization. B200's 192 GB supports full-precision large models. Multi-GPU setups mitigate V100 constraints.
Which is cheaper to rent, the B200 or the V100?▾
Cloud rental prices for both the B200 and V100 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 V100?▾
The B200 has 192 GB of HBM3e memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find B200 and V100 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 V100?▾
The B200 uses the Blackwell architecture (2024) while the V100 uses Volta (2017). The B200 delivers 36.0x the FP16 throughput and 8.9x the memory bandwidth of the V100.