The NVIDIA H100 has been the go-to GPU for production AI workloads for years.
Now, the B200 is here with significantly more memory, higher bandwidth, and Blackwell's newer Tensor Core architecture.
So should you upgrade?
Not necessarily.
The real question isn't “Which GPU is faster?”
It's “Which GPU gives my workload the best performance per dollar?”
H100 vs B200: What Actually Changes?
The biggest difference is memory.
- H100: 80GB HBM3, ~3.35 TB/s bandwidth
- B200: 192GB HBM3e, ~8 TB/s bandwidth
- H100: Hopper architecture
- B200: Blackwell architecture
- H100: FP8 acceleration
- B200: FP4 + FP8 acceleration That 192GB of VRAM changes what you can run on a single GPU. Larger models and long-context workloads that require multiple H100s can potentially fit across fewer B200 GPUs, reducing the complexity and communication overhead of multi-GPU serving.
Is B200 Worth Paying More?
This is where things get interesting.
On Packet.ai, the B200 currently starts at $3.75/GPU-hour, while H100 starts at $2.50/GPU-hour.
So the B200 costs more per hour.
But GPU-hour isn't the metric that matters most for AI inference.
What matters is cost per useful output such as tokens generated, training progress, or jobs completed.
If your workload comfortably fits on an H100, paying for B200 may not make financial sense.
But if you're running 70B+ models, long-context inference, high concurrency, or workloads that benefit from FP4, B200's additional memory and compute can make the higher hourly price worthwhile.
Which GPU Should You Choose?
Choose H100 if:
- Your model fits comfortably within 80GB
- You're running smaller or mid-sized LLMs
- GPU-hour cost is your primary constraint
- You're already operating Hopper infrastructure
- You don't need Blackwell's FP4 capabilities
Choose B200 if:
- You're serving 70B+ models
- Long-context inference is important
- You need significantly more VRAM
- High throughput matters more than hourly GPU price
- You're optimizing for cost per token at scale
The Bottom Line
The B200 isn't automatically a better choice just because it's newer.
For some workloads, H100 remains the smarter economic choice.
For larger models and high-throughput inference, B200's additional memory and Blackwell architecture can make it the better long-term investment.
The right comparison isn't H100 vs B200 on a spec sheet.
It's H100 vs B200 for your workload, utilization, and cost per output.
Want the Full Comparison?
We've broken down the architecture, benchmarks, model sizing, pricing, CUDA compatibility, cooling requirements, and cost-per-token economics in our complete guide:
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