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
Superior FP16 performance on the B200 at 4500 TFLOPS over H100 NVL's 1979 TFLOPS accelerates deep learning training by handling more floating-point operations per second, reducing epoch times for large models. FP32 at 90 TFLOPS on B200 edges out H100 NVL's 67 TFLOPS, benefiting simulations requiring precise single-precision math. In inference scenarios, B200's FP8 capability of 9000 TFLOPS doubles H100 NVL's 3958 TFLOPS, enabling quantized models to process queries faster.
Memory bandwidth defines scalability: B200's 8000 GB/s supports batch sizes up to 2.4 times larger than H100 NVL's 3350 GB/s, minimizing data transfer bottlenecks in transformer training. This proves critical for LLMs exceeding 100 billion parameters, where H100 NVL might require model parallelism. Higher TDP of 1000W on B200 versus 700W on H100 NVL demands robust cooling but unlocks peak efficiency in sustained workloads.
Real-world impact emerges in multi-GPU clusters: B200 leverages NVLink and PCIe 6.0 for faster interconnects than H100 NVL's PCIe 5.0, reducing latency in distributed training.
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 NVL
| 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
Opt for NVIDIA B200 SXM in scenarios requiring massive VRAM, such as training LLMs over 500 billion parameters on a single GPU with its 192 GB HBM3e. The 8000 GB/s bandwidth sustains enormous batch sizes without spilling to slower storage, ideal for research labs pushing model scales.
High-throughput inference benefits from 9000 TFLOPS FP8, serving enterprise deployments where latency under 100 ms matters across thousands of users.
When to Choose the H100 NVL
NVIDIA H100 NVL suits cost-conscious deployments with its lower entry pricing at $1.40 per hour versus B200 SXM's $1.71 per hour. Teams handling models under 70 billion parameters find 94 GB HBM3 sufficient, avoiding B200's higher average $4.60 per hour cost.
Power-limited environments favor H100 NVL's 700W TDP over 1000W, fitting standard data centers without infrastructure overhauls while delivering 1979 TFLOPS FP16 for reliable production inference.
Use Cases
B200's 192 GB VRAM and 8000 GB/s bandwidth handle massive datasets and batch sizes infeasible on H100 NVL's 94 GB and 3350 GB/s. FP16 at 4500 TFLOPS cuts training time significantly.
9000 TFLOPS FP8 on B200 doubles H100 NVL's 3958 TFLOPS for quantized serving. Higher memory capacity supports longer contexts without sharding.
B200's 90 TFLOPS FP32 and vast VRAM accelerate parameter-efficient tuning on large base models. It outperforms H100 NVL's 67 TFLOPS for faster convergence.
Both GPUs excel with ample VRAM for high-resolution generation; H100 NVL suffices at lower $1.40 per hour cost, while B200 boosts throughput via 4500 TFLOPS FP16.
H100 NVL's 67 TFLOPS FP32 and 700W TDP fit simulations efficiently at $2.89 per hour average. B200's power draw proves excessive for non-AI numerics.
Frequently Asked Questions
Which GPU has more VRAM: B200 SXM or H100 NVL?▾
NVIDIA B200 SXM provides 192 GB HBM3e, more than double H100 NVL's 94 GB HBM3. This enables single-GPU handling of models up to 500 billion parameters. Bandwidth at 8000 GB/s on B200 further enhances data movement.
How do B200 and H100 compare in FP16 performance?▾
B200 delivers 4500 TFLOPS FP16 versus H100 NVL's 1979 TFLOPS, a 2.3 times speedup for training. This translates to shorter epochs in deep learning pipelines. FP8 follows suit at 9000 TFLOPS on B200 against 3958 TFLOPS.
What are the cloud rental prices for these GPUs?▾
B200 SXM starts at $1.71 per hour with $4.60 average across 13 offers. H100 NVL begins at $1.40 per hour averaging $2.89 across 9 offers. Pricing varies by provider and region.
Does B200 use more power than H100 NVL?▾
B200 SXM has a 1000W TDP compared to H100 NVL's 700W. This supports higher compute but requires advanced cooling. Efficiency gains offset in dense AI clusters.
Which is better for large batch sizes?▾
B200 SXM excels with 8000 GB/s bandwidth over H100 NVL's 3350 GB/s, supporting 2.4 times larger batches. This reduces training iterations for LLMs. VRAM disparity reinforces the advantage.
What interconnects do they support?▾
Both offer NVLink, PCIe, and InfiniBand. B200 adds PCIe 6.0 versus H100 NVL's PCIe 5.0 for lower latency in multi-node setups. Form factors include SXM and NVL.
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.