Specifications Compared
| Spec | B200 | GB300 |
|---|---|---|
| TDP | 1000W | 1400W |
| VRAM | 192 GB | 288 GB |
| CUDA Cores | 18,432 | |
| Memory Type | HBM3e | HBM3e |
| Architecture | Blackwell | Blackwell Ultra |
| Form Factors | SXM, NVL | SXM |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | NVSwitch, NVLink |
| Tensor Cores | 576 | |
| FP8 Performance | 9,000 TFLOPS | 4,500 TFLOPS |
| FP16 Performance | 4,500 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 90 TFLOPS | 90 TFLOPS |
| FP64 Performance | 45 TFLOPS | 45 TFLOPS |
| INT8 Performance | 9,000 TOPS | 4,500 TOPS |
| Memory Bandwidth | 8,000 GB/s | 12,000 GB/s |
Performance Analysis
The B200 NVL excels in compute-intensive tasks: 4500 TFLOPS FP16 and 9000 TFLOPS FP8 dwarf the GB300 SXM6's 2250 TFLOPS FP16 and 4500 TFLOPS FP8, enabling twice the throughput for AI training where tensor operations dominate. This FP16 to FP32 ratio of 50:1 on B200 accelerates model convergence in mixed-precision workflows. At 1000W TDP, it delivers higher efficiency than GB300's 1400W draw.
GB300 SXM6 prioritizes memory: 288 GB VRAM supports larger batch sizes in inference, reducing per-token latency for trillion-parameter models, while 12000 GB/s bandwidth cuts data transfer bottlenecks by 50 percent over B200's 8000 GB/s. In training, this aids memory-bound scenarios like fine-tuning with extended contexts, though lower FP8 limits sparse acceleration.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B200 NVL
| 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 |
When to Choose the B200 NVL
Select the B200 NVL for production AI training and inference today. Its 4500 TFLOPS FP16 outperforms GB300 SXM6 by 100 percent, suiting workloads like LLM pre-training on datasets fitting within 192 GB VRAM. Availability at $10.50 per hour enables rapid scaling without waiting for GB300 deployments.
When to Choose the GB300 SXM6
Opt for GB300 SXM6 in memory-constrained environments. The 288 GB HBM3e VRAM and 12000 GB/s bandwidth handle massive models without multi-GPU sharding, ideal for high-concurrency inference servers. It future-proofs against growing model sizes despite higher 1400W TDP.
Use Cases
B200 NVL's 4500 TFLOPS FP16 doubles GB300 SXM6's 2250 TFLOPS, speeding convergence on large datasets. Lower 1000W TDP ensures denser clusters.
GB300 SXM6's 288 GB VRAM supports bigger batches for trillion-parameter models versus B200 NVL's 192 GB limit. 12000 GB/s bandwidth reduces latency.
B200 NVL's 9000 TFLOPS FP8 accelerates parameter-efficient fine-tuning twice as fast as GB300 SXM6's 4500 TFLOPS. Availability at $10.50 per hour aids quick experiments.
Both handle diffusion models well within 192-288 GB VRAM; B200 NVL offers higher FP16 for generation speed, GB300 SXM6 more headroom for high-res variants.
B200 NVL's 90 TFLOPS FP32 matches GB300 SXM6, but superior interconnects like PCIe 6.0 suit simulations. Lower TDP optimizes sustained HPC runs.
Frequently Asked Questions
What is the VRAM difference between NVIDIA B200 NVL and GB300 SXM6?▾
NVIDIA B200 NVL has 192 GB HBM3e VRAM, while GB300 SXM6 doubles it to 288 GB. This enables GB300 to load larger models without partitioning. Memory bandwidth follows suit at 8000 GB/s for B200 NVL versus 12000 GB/s.
Which GPU has higher FP16 performance?▾
B200 NVL leads with 4500 TFLOPS FP16 compared to GB300 SXM6's 2250 TFLOPS. Both share 90 TFLOPS FP32. This favors B200 NVL for training acceleration.
What are the power requirements?▾
B200 NVL consumes 1000W TDP, lower than GB300 SXM6's 1400W. Efficiency gains make B200 NVL preferable for dense racks. Form factors include SXM and NVL for B200.
Is GB300 SXM6 available in the cloud?▾
No live offers exist for GB300 SXM6 currently. B200 NVL starts at $10.50 per hour across one provider. Monitor gpuperhour.com for updates.
How do interconnects compare?▾
B200 NVL supports NVLink, PCIe 6.0, and InfiniBand; GB300 SXM6 uses NVSwitch and NVLink. NVSwitch enhances multi-GPU scaling on GB300. Both excel in AI clusters.
Which is better for large model inference?▾
GB300 SXM6 excels with 288 GB VRAM for high batch sizes. B200 NVL's 4500 TFLOPS FP16 suits compute-bound serving. Choose based on model scale.
Which is cheaper to rent, the B200 or the GB300?▾
Cloud rental prices for both the B200 and GB300 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 GB300?▾
The B200 has 192 GB of HBM3e memory. The GB300 has 288 GB of HBM3e memory.
Can I find B200 and GB300 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 GB300?▾
The B200 uses the Blackwell architecture (2024) while the GB300 uses Blackwell Ultra (2025). The B200 delivers 2.0x the FP16 throughput and 1.5x the memory bandwidth of the GB300.