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
Performance gaps translate directly to real-world advantages for the B200. Its 4500 TFLOPS FP16 rate, 36 times the V100's 125 TFLOPS, accelerates deep learning training where half-precision dominates. FP32 at 90 TFLOPS on B200, over five times the V100's 15.7 TFLOPS, enhances precision-sensitive simulations and inference pipelines.
Memory specs reshape workload feasibility: 192 GB VRAM on B200 supports batch sizes impossible on V100's 16 GB, preventing out-of-memory errors in large LLMs. The 8000 GB/s bandwidth, nearly nine times 900 GB/s, minimizes data transfer delays, enabling larger effective batches and faster iterations in training loops.
Power demands differ too: B200's 1000W TDP requires enterprise cooling, while V100's 300W fits modest setups. These factors position B200 for high-throughput production, V100 for lighter duties.
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 16GB
| 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
Select the B200 SXM for large-scale LLM training or inference. Its 192 GB VRAM loads models exceeding 100 billion parameters, and 4500 TFLOPS FP16 cuts training time dramatically. FP8 performance at 9000 TFLOPS suits high-volume serving.
Modern frameworks optimized for Blackwell excel here, leveraging NVLink and PCIe 6.0 for multi-GPU scaling unavailable on older V100 interconnects.
When to Choose the Tesla V100 16GB
Choose V100 16GB for budget prototyping or legacy applications. At $0.10 per hour, it handles small models with 125 TFLOPS FP16 efficiently, suiting experimentation.
It fits environments with 300W power limits or software tied to Volta, avoiding B200's $1.71 per hour cost for non-demanding tasks.
Use Cases
B200's 192 GB VRAM and 4500 TFLOPS FP16 manage massive datasets and models, far beyond V100's 16 GB and 125 TFLOPS.
9000 TFLOPS FP8 and 8000 GB/s bandwidth deliver high throughput for production serving, unlike V100's limitations.
Superior 90 TFLOPS FP32 and memory capacity speed up iterations on large pre-trained models.
192 GB VRAM supports high-resolution generations and batch processing without memory constraints.
V100's 15.7 TFLOPS FP32 and low $0.10/hr cost suffice for modest simulations; B200 overkill unless scaling massively.
Frequently Asked Questions
What is the VRAM capacity of B200 SXM versus V100 16GB?▾
The B200 SXM offers 192 GB HBM3e VRAM, while V100 16GB provides 16 GB HBM2. This 12-fold difference allows B200 to accommodate far larger models without swapping.
How do FP16 performance levels compare?▾
B200 achieves 4500 TFLOPS FP16, 36 times higher than V100's 125 TFLOPS. This boosts AI training and inference speeds significantly.
What are current cloud rental prices?▾
B200 SXM rents from $1.71 per hour, averaging $4.60 across 13 offers. V100 16GB starts at $0.10 per hour, averaging $0.82 over 28 offers.
Does B200 outperform in FP32 for training?▾
Yes, B200 delivers 90 TFLOPS FP32 versus V100's 15.7 TFLOPS, a 5.7x gain aiding mixed-precision training tasks.
What are the TDP ratings?▾
B200 SXM has a 1000W TDP, demanding robust power setups. V100 16GB uses 300W, suitable for standard servers.
How do memory bandwidths differ?▾
B200 provides 8000 GB/s, nearly 9 times V100's 900 GB/s. Higher bandwidth reduces bottlenecks in data-intensive workloads.
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.