Is a B200 worth its rental premium over an H100? Only if it runs your job 2× faster

Is a B200 worth its rental premium over an H100? Only if it finishes your job faster than it costs more, or if your model doesn't fit in 80GB. On Nov 3, 2026 a B200 rented for $7.99 an hour on Runpod Secure Cloud against $3.99 for an H100 SXM, exactly 2× the price. At Lambda the gap was 1.6×, at Nebius 1.9× and at CoreWeave 1.4×. NVIDIA's own spec sheets put one B200 at about 2.3× an H100's FP8 compute, 2.4× its memory bandwidth and 2.25× its memory. So on paper a fully busy B200 is slightly cheaper per token at most managed clouds. In practice it only wins if your software really runs about twice as fast on it, and it loses outright for small models, idle time, and on Vast.ai's marketplace, where cheap H100s make the gap 3.6×. For today's prices, see our live cloud-GPU compare page.

Below the price line, a B200 must do more than that many H100s' worth of work per hour to come out cheaper per token. NVIDIA's spec ratio is about 2.3×, so the margin is real at CoreWeave and Lambda, thin at Runpod and Nebius, and gone on Vast.

How much does a B200 cost to rent vs an H100?

About $6 to $9 an hour for a B200 and $2.40 to $6.16 for an H100, by provider, on Nov 3, 2026. The Vast and Runpod figures are from our hourly read of their APIs (20:20 UTC); every other price is the provider's own published on-demand rate, read the same day.

Two things moved since our last read. Runpod's B200 used to be quoted at $5.98: that's still the Community Cloud price in its page's structured data, but the Secure Cloud price it lists is $7.99. And B200 supply on marketplaces is still thin: our Nov 3 read found two on-demand B200 offers on Vast, from $8.75, and interruptible ones from $7.50.

What does the B200's premium actually buy?

More memory, more bandwidth and more compute, by roughly the same factor. NVIDIA publishes the B200's figures for 8-GPU systems, so the per-GPU numbers below are those divided by eight.

Sources: NVIDIA's DGX B200 page lists "1,440 GB total, 64 TB/s HBM3e bandwidth" for 8 GPUs; its HGX page lists HGX B200 at 72 petaFLOPS FP8 and 36 BF16 (sparse) and 144 sparse / 72 dense FP4; its H100 page lists the SXM at 80GB, 3.35TB/s and 3,958 teraFLOPS FP8 "With sparsity". The ratios are our arithmetic. Rental providers list the B200 at 179 to 180GB, except Hyperstack, which lists 192GB.

What about NVIDIA's "15X the inference performance of DGX H100"? It's on the DGX B200 page, and its footnote says "Projected performance subject to change", measured per GPU at a 32,768-token input with a 50ms token-to-token latency target. The same page's cost claim, Blackwell "roughly 4.5x cheaper" per token than Hopper, compares TensorRT-LLM on Blackwell with vLLM on Hopper on one model (GPT-OSS-120B). Those are best-case setups chosen by the seller. Plan with the spec ratios, then measure your own job.

When is a B200 cheaper per token than an H100?

When it runs your job faster than its price ratio. The rule is one line of arithmetic: cost per token = price per hour ÷ tokens per hour. At Runpod Secure Cloud ($7.99 vs $3.99), a B200 that does 2.5× the H100's tokens costs 20% less per token; one that does 1.5× costs 33% more. At Lambda's 1.6× ratio, the B200 only has to be 1.6× faster to break even.

Whether your job gets that speedup depends on three things:

• Is the card busy? Per-token math only counts the minutes it's generating. A pod you leave open while you read logs is billed by the hour, and a B200's idle hour costs double an H100's. For one person testing prompts, idle time usually decides it, and the H100 wins.
• Is the job big enough? Large models and large batches stretch the B200's bandwidth and compute; a small model at batch size 1 can't use them, so the speedup lands well below 2×. On a 7B or 8B model, both cards are overkill: an H100 already is, and a cheaper 24GB card usually is too.
• Does your software use Blackwell? Support is newer. PyTorch added Blackwell support in version 2.7 (released 2025) as a prototype feature, with pre-built CUDA 12.8 wheels, and the B200's FP4 speed needs a model and serving stack that run in FP4. An image built for an H100 on an older CUDA may not run on a B200 at all, or may run without the new paths. Check your container's CUDA and framework versions before you rent.

The cheapest way to know: rent each card for an hour, run the same job, and divide the price by the work done. On Runpod Secure Cloud that test costs about $12 ($7.99 + $3.99), by our arithmetic.

When does a B200 win outright?

When your model doesn't fit in 80GB. Then the comparison isn't one B200 against one H100, it's one B200 against two or three H100s. A rough rule: at 8-bit precision each billion parameters takes about 1GB for the weights, plus room for the context (KV cache). A model of around 100 to 150 billion parameters at 8-bit needs more than one H100 but can fit on a single B200's 180GB; use our VRAM calculator for your exact model and context length.

• Runpod Secure Cloud: two H100 SXMs cost $7.98 an hour, one B200 $7.99. Same price, but the B200 keeps the model on one card with no splitting between GPUs.
• Lambda: two H100 SXMs cost $8.38 an hour ($4.19 each on its 2x plan), one B200 $6.99. The B200 is cheaper.
• Three H100s (for models that need more than 160GB) cost more than one B200 at every managed provider in our table. Only on Vast's marketplace were three of the cheapest H100 offers ($7.20 together) under its cheapest B200 ($8.75), and it had just three H100 SXM offers that day.

Splitting a model across GPUs works, but it adds setup, and traffic between cards slows each token. See multi-GPU setups for how that trade-off works.

One more option on the same page: Runpod listed a B300 with 288GB at $8.99 an hour on Nov 3, 2026, $1 more than its B200. On Vast our same read had one on-demand B300 offer at $7.89, below its cheapest B200. If you're renting for memory, compare that too.

Is a B200 worth it for fine-tuning or training?

For a big run that keeps it busy for hours, often yes; for a small LoRA, no. Training is the workload that keeps a GPU fully fed, so the per-token logic applies cleanly: if your run finishes in less than half the time on a B200 at Runpod's 2× price, it's cheaper overall, and the 180GB lets you use bigger batches or skip some memory-saving tricks. A two-hour QLoRA fine-tune of an 8B model, by contrast, fits comfortably on one H100 or smaller, and the B200's extra speed saves you minutes at double the hourly rate. Our guide to the best cloud GPU for fine-tuning matches card to job size.

For multi-GPU training, CoreWeave's 8-GPU B200 node ($68.80 an hour) costs 1.4× its H100 node ($49.24), the smallest ratio in our table, because both are premium data center nodes. That's the setting where NVIDIA's speedups are most likely to show up.

Rent the H100 unless you need the B200's memory or can keep it busy, and then compare cost per token, not per hour. More on the same decision: the cheapest H100 rental, what an H200 costs to rent, the full cloud-GPU pricing guide, and how to run Llama 4 in the cloud for a worked model-fit example.