B200 vs H100 for LLM Inference: Which GPU Should You Rent?
The B200 has more memory and far more memory bandwidth than the H100, and it costs more per hour. Whether that's a good trade depends on your model, your traffic and one number: cost per million tokens. A GPU that's twice as fast but three times the price is the more expensive way to serve a token.
This guide compares the two for LLM inference, shows when the B200's extra memory lets you use fewer GPUs, and gives you a simple way to test both on your own workload. Figures are as of November 2026. Prices and throughput numbers in the examples are hypothetical or taken from documentation samples, so check the Nebula Block console for real rates.
The specs that matter for inference
LLM decoding is mostly limited by how fast a GPU can read its weights from memory, and by how much memory it has for the model and the KV cache. Two numbers matter most: memory capacity and memory bandwidth. Compute matters too, but for serving it usually comes second.
The H100 figures come from NVIDIA's H100 datasheet. NVIDIA lists the HGX B200 at 180 GB per GPU, and server vendors quote roughly 8 TB/s of bandwidth (some list 7.7 TB/s, and some B200 products are sold with 192 GB). Treat the B200 numbers as approximate and confirm what a given rental includes.
One detail matters for renters: the H100 comes in two forms. Nebula Block's API documentation uses an H100 PCIe as its sample configuration, and that card has noticeably less memory bandwidth than the SXM version. Before comparing prices, make sure you know which H100 you're looking at.
What the extra memory buys: fewer GPUs
Capacity decides how many GPUs a model needs, and that often matters more than raw speed. Using the numbers from our VRAM guide for Llama 3.3 70B at 8K context with 10 concurrent chats, and planning to use about 90% of each card's memory:
In FP8, one B200 holds what takes two H100s. That removes tensor parallelism and the cross-GPU traffic that comes with it, and it simplifies operations. (When you split a model across GPUs, the GPU count must divide the model's attention heads, which is why BF16 on H100s is four cards, not three.) Long contexts and high concurrency push the same way, because the KV cache grows with both.
The flip side: if your model fits comfortably on one H100, the B200's extra capacity sits unused unless you raise concurrency or context length to use it.
The cost question: how much faster does the B200 need to be?
Compare cost per token, not hourly price:
cost per million tokens = hourly price ÷ (tokens per second × 3,600) × 1,000,000
The B200 beats the H100 on cost per token when its price ratio is lower than its throughput ratio. Nebula Block's API docs include two sample prices: $2.632 per hour for a single H100 PCIe VM in Canada, and $57.20 per hour for an 8-GPU B200-NVLink node in the United States, which is $7.15 per GPU. Those samples differ in region and form factor, so they're not a like-for-like quote, but they illustrate the arithmetic: $7.15 ÷ $2.632 is about 2.7. At those sample prices, a B200 would need roughly 2.7 times the H100's tokens per second to match its cost per token.
Here's what that looks like with a hypothetical H100 throughput of 1,000 tokens per second (made up, to show the method):
For a sense of how much speed-up is plausible, compare memory bandwidth, since decoding is often bandwidth-limited: about 8 TB/s on the B200 against 3.35 TB/s on the H100 SXM is roughly 2.4 times, and against the 2 TB/s of an H100 PCIe it is 4 times. That's a rough guide, not a prediction. Real throughput depends on the model, batch size, context length, precision and how well your serving engine supports the hardware.
That last point deserves a check. vLLM's FP8 documentation lists Hopper and Ada Lovelace as officially supported for W8A8, so confirm that your engine version supports Blackwell, and any FP4 path you want to use, before you plan around it.
When the H100 is the better rental
- Your model fits on one H100 with room to spare. You'd pay for B200 capacity you can't use.
- Traffic is light or spiky. At low utilization, hourly price dominates, and the cheaper GPU wins. If utilization is that low, also consider whether serverless inference is cheaper still.
- You're optimizing for a tight budget or a short experiment. An hour on a cheaper GPU costs less to find out.
- You rely on features that are better proven on Hopper. Check your serving stack's notes for the B200.
When the B200 is the better rental
- The model is large, or the context is long. More memory means fewer GPUs and less splitting.
- Concurrency is high. More room for KV cache means more simultaneous requests per GPU.
- Latency matters. Higher bandwidth can cut time per token on memory-bound decoding, which you should confirm in your own test.
- You want a simpler deployment. One GPU instead of two or four means fewer moving parts.
What to know about renting each on Nebula Block
- Hardware and regions. Nebula Block's GPU Cloud lists B300 and B200 as Blackwell-class options for training and inference, and the H100 for large-scale training, also offered as container and bare metal. Configurations run from a single GPU to 8-GPU nodes, and stock varies by region, so check the Deploy page.
- Spending limits. Every account has an hourly spending limit. A multi-GPU node at the sample B200 price is well above what a new account starts with, so request an increase early rather than waiting for a failed deploy.
- Billing. Instances are billed for as long as they exist, powered on or not, and stopping one doesn't stop the charge. Terminate what you're done with.
- Single-GPU or node pricing. The B200 sample is a full 8-GPU node. If you only need one or two GPUs, check which configurations are in stock and what they cost, because a full node would be an expensive way to run a small test.
How to decide in an afternoon
- Size the model with the VRAM guide to see how many GPUs each option needs.
- Rent each for a short test, with the same model, precision and serving settings.
- Load test at your real concurrency, and record tokens per second and p95 latency for each.
- Compute cost per million tokens with the formula above, using the real hourly price for the exact configuration.
- Pick the cheaper token that meets your latency target. If they're close, take the simpler deployment.
Our guide to cutting inference costs covers what to tune before and after you choose, and the Bare-Metal vs Cloud GPU Instances guide shows the same break-even logic for virtualized versus dedicated hardware.
The bottom line
The B200 is the better GPU on paper, with far more memory and bandwidth. It's the better rental only if that advantage turns into more tokens per dollar for your workload, either through higher throughput or by letting you use fewer GPUs. Do the arithmetic with real prices and a real test, and let the cost per million tokens decide.
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- Email: contact@nebulablock.com
- Website: nebulablock.com
- Docs: docs.nebulablock.com
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