Specifications Compared
| Spec | B200 | P100 |
|---|---|---|
| TDP | 1000W | 250W |
| VRAM | 192 GB | 16 GB |
| CUDA Cores | 18,432 | 3,584 |
| Memory Type | HBM3e | HBM2 |
| Architecture | Blackwell | Pascal |
| Form Factors | SXM, NVL | SXM2, PCIe |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | NVLink |
| Tensor Cores | 576 | |
| FP8 Performance | 9,000 TFLOPS | |
| FP16 Performance | 4,500 TFLOPS | 9.3 TFLOPS |
| FP32 Performance | 90 TFLOPS | 9.3 TFLOPS |
| FP64 Performance | 45 TFLOPS | 4.7 TFLOPS |
| INT8 Performance | 9,000 TOPS | |
| Memory Bandwidth | 8,000 GB/s | 732 GB/s |
Performance Analysis
The B200's FP16 throughput of 4500 TFLOPS enables rapid large-scale model training, a 484-fold improvement over the P100's 9.3 TFLOPS, reducing epochs from days to hours for deep learning pipelines. FP32 performance follows suit at 90 TFLOPS on the B200 versus 9.3 TFLOPS on the P100, benefiting simulations and precise computations. FP8 at 9000 TFLOPS on the B200 further accelerates inference for quantized models.
Memory differences profoundly impact workloads: the B200's 192 GB VRAM supports massive batch sizes for LLMs exceeding 100 billion parameters, while the P100's 16 GB limits batches to small models. Bandwidth of 8000 GB/s on the B200 minimizes data bottlenecks during gradient updates, unlike the P100's 732 GB/s which constrains throughput in memory-intensive tasks.
Power consumption reveals trade-offs: the B200's 1000W TDP demands robust cooling, contrasting the P100's efficient 250W, influencing deployment in edge or low-power clusters.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B200
| 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 |
P100
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 2×NVIDIA Tesla P100 16GB VRAM | 16GB | 0 vCPU 256GB RAM 960GB Storage | Netherlands | $0.60/GPU/hr $1.20/hr total (2×) | Available |
When to Choose the B200
The B200 excels in modern AI training and inference where high VRAM and compute are essential. For LLMs requiring over 16 GB, its 192 GB HBM3e handles full model loading without sharding. Bandwidth of 8000 GB/s supports large batches, cutting training time significantly compared to the P100.
When to Choose the P100
The P100 fits budget-conscious or legacy deployments with low power needs. At $0.07 per hour, it handles small-scale inference or prototyping economically. Its 250W TDP suits environments without high-power infrastructure, and 9.3 TFLOPS suffices for non-demanding scientific tasks.
Use Cases
The B200's 4500 TFLOPS FP16 and 192 GB VRAM manage massive datasets and models, slashing training times versus the P100's 9.3 TFLOPS and 16 GB limits.
9000 TFLOPS FP8 on the B200 delivers low-latency serving for large models; P100's 16 GB VRAM restricts it to tiny models.
B200's 8000 GB/s bandwidth accelerates gradient computations on full parameter sets, outperforming P100's 732 GB/s for efficient iterations.
192 GB VRAM on B200 supports high-resolution generations without swapping; P100 struggles with 16 GB on complex prompts.
P100's 9.3 TFLOPS FP32 and low $0.07 per hour cost suit modest simulations; B200's power draw is excessive for non-AI tasks.
Frequently Asked Questions
How much faster is the B200 than the P100 in FP16?▾
The B200 achieves 4500 TFLOPS in FP16, over 484 times the P100's 9.3 TFLOPS. This translates to dramatically shorter training runs for AI models. Real-world speedups depend on memory-bound tasks.
What is the VRAM difference between B200 and P100?▾
The B200 offers 192 GB HBM3e versus the P100's 16 GB HBM2, a 12-fold increase. This enables larger models on B200 without distributed setups. Bandwidth also jumps from 732 GB/s to 8000 GB/s.
Is the P100 still viable for cloud use?▾
Yes, at $0.07 per hour average $0.25, it serves prototyping or light inference. Its 250W TDP fits low-power needs. However, it cannot handle modern LLMs due to 16 GB VRAM.
What architectures do B200 and P100 use?▾
B200 uses Blackwell from 2024; P100 uses Pascal from 2016. This eight-year gap yields massive compute gains like 90 TFLOPS FP32 on B200. Interconnects include NVLink on both but PCIe 6.0 on B200.
How do power requirements compare?▾
B200 demands 1000W TDP, requiring enterprise cooling, while P100 uses 250W for efficiency. This affects cloud pricing and deployment feasibility. B200's performance justifies the draw for heavy workloads.
Current cloud prices for B200 vs P100?▾
B200 starts at $1.71 per hour averaging $4.61 across 16 offers; P100 at $0.07 averaging $0.25 across 3. Prices reflect capability gaps. Check gpuperhour.com for live updates.
Which is cheaper to rent, the B200 or the P100?▾
Cloud rental prices for both the B200 and P100 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 P100?▾
The B200 has 192 GB of HBM3e memory. The P100 has 16 GB of HBM2 memory.
Can I find B200 and P100 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 P100?▾
The B200 uses the Blackwell architecture (2024) while the P100 uses Pascal (2016). The B200 delivers 483.9x the FP16 throughput and 10.9x the memory bandwidth of the P100.