B200 vs H100
Explore a head to head comparison of specifications, performance, and pricing.
B200
The NVIDIA B200 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H100
The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.
B200 vs H100: Which Should You Choose?
The B200 offers 192 GB of VRAM — 2× the 80 GB on the H100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H100 delivers 267.6 TFLOPS versus 1 TFLOPS on the B200 — 268× faster for mixed-precision training and inference. Memory bandwidth favors the B200 at 0.01 TB/s compared to 0.00 TB/s on the H100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the B200 is built on Blackwell while the H100 uses Hopper, reflecting different generational capabilities and optimizations. On Shadeform, the H100 starts from $1.66/hr versus $5.29/hr for the B200 — 219% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 4 for the B200, giving more options for region and pricing flexibility.
B200 — Best Use Cases
- •Next-generation LLM pre-training at scale
- •Trillion-parameter model inference
- •Ultra-high-throughput AI workloads
- •Advanced HPC and scientific computing
Choose B200 when:
- ✓You need 192 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
H100 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H100 when:
- ✓80 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the B200 & H100 compare
Compare detailed hardware specifications and average pricing for the B200 and H100.
Compare Hardware Specifications
| B200 | H100 | |
|---|---|---|
| GPU Type | B200 | H100 |
| VRAM per GPU | 192 GB | 80 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell | Hopper |
| Interconnect | SXM6 | PCIe Gen5 or SXM5 |
| Memory Bandwidth | 8 TB/s | 3.35 TB/s |
| FP16 TFLOPS | 1,191.2 TFLOPS (16:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 20480 | 16896 |
| Tensor Cores | 640 (5th Gen) | 528 (4th Gen) |
| Base Clock | 700 MHz | 1365 MHz |
| Boost Clock | 1965 MHz | 1785 MHz |
| TDP | 1000W | 350-700W |
| Process Node | TSMC 4NP | TSMC 4N |
| Data Formats | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| B200 | H100 | |
|---|---|---|
| 1 GPU | $5.29 /hr | $2.85 /hr |
| 2 GPUs | $10.49 /hr | $5.19 /hr |
| 4 GPUs | $20.78 /hr | $9.79 /hr |
| 8 GPUs | $36.68 /hr | $19.35 /hr |
Frequently Asked Questions: B200 vs H100
The main differences are VRAM (192 GB vs 80 GB), FP16 throughput (1 vs 267.6 TFLOPS), architecture (Blackwell vs Hopper). The B200 uses the Blackwell architecture while the H100 is based on Hopper, giving each GPU different generational capabilities.
The H100 is generally better for large language model training due to its higher throughput and 80 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the H100 is available from $1.66/hr. The B200 starts from $5.29/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The B200 has more VRAM at 192 GB, compared to 80 GB on the H100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H100 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the B200, paying the premium may be justified by faster job completion and lower total cost.
The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 4 for the B200. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either B200 or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore B200 & H100 Instances
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