NVIDIA B200 for Inference: When the Throughput Justifies the Price

The first question every team asks about the B200 for inference is whether the throughput gain pays back the price gap. At $4.00/GPU-hour, the B200 costs $1.40/hr more than an H200 and $2.00/hr more than an H100, so the card has to earn that premium in tokens per second, not in spec-sheet bragging rights. The B200 is the right rental only when its higher throughput cuts your cost per token below what an H200 delivers, and that happens at specific batch sizes and model footprints, not universally. This article gives you the break-even math, the B200 vs H200 inference comparison, and a clear rule for when to move up.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware.

What the B200 changes over the H200

The H200 is a Hopper card with 141GB of HBM3e and 4.80 TB/s of bandwidth. The B200 is a Blackwell card with 180GB of HBM3e and 8.0 TB/s of bandwidth. Two numbers drive the entire decision.

• Memory bandwidth: 8.0 TB/s on the B200 versus 4.80 TB/s on the H200, a 1.67x increase. Decode-heavy LLM serving is bandwidth-bound, so this is where the B200 earns its throughput.
• Memory capacity: 180GB versus 141GB, a 28% increase. That extra headroom holds larger weights or more KV cache before you have to shard across cards.
• Compute and FP4: Blackwell adds native FP4 support and roughly doubles low-precision matrix throughput, which lifts prefill on compute-bound, large-batch workloads.

The practical read is that Blackwell inference performance scales with bandwidth and low-precision compute. If your serving loop is memory-bound during decode, the 1.67x bandwidth advantage translates fairly directly into higher tokens per second per card. If your workload is small and already underutilizes an H200, the extra card will not help, because you are paying for throughput you never reach.

The break-even math: cost per token, not cost per hour

The hourly rate is the wrong unit for this decision. What matters is the B200 cost per token, which is the hourly rate divided by the tokens that card actually produces per hour. The card with the lower cost per token wins, even when its sticker price is higher.

Work a real example. Assume a 70B model in FP8 serving at high concurrency where decode is bandwidth-bound:

GPU	Bandwidth	Price	Decode throughput (tok/s)	Cost per 1M tokens
H100	3.35 TB/s	$2.00/hr	2,800	$0.198
H200	4.80 TB/s	$2.60/hr	4,100	$0.176
B200	8.0 TB/s	$4.00/hr	7,200	$0.154

The throughput figures above are illustrative of a bandwidth-bound decode profile, where tokens per second scale roughly with memory bandwidth. Plug in your own measured numbers before committing. The logic is what matters.

Read the B200 vs H200 inference row carefully. The B200 costs 54% more per hour than the H200 ($4.00 versus $2.60), so to break even on cost per token it has to produce at least 54% more tokens per second. In this profile the B200 delivers about 76% more throughput than the H200 (7,200 versus 4,100), which clears the bar. Cost per million tokens drops from $0.176 to $0.154, roughly 12% cheaper, so the B200 is the cheaper card in production despite the higher rate.

Now invert it. If your B200 only reaches 6,000 tok/s because batches are small or context is short, the cost per million tokens rises to about $0.185, which is worse than the H200. The break-even point is exact: at $4.00/hr versus $2.60/hr, the B200 must hit roughly 1.54x the H200's throughput or it loses on cost per token. Below that line you are paying a premium for capacity you cannot fill.

The rule in one sentence

Rent the B200 for inference when your measured decode throughput on it exceeds 1.54x your H200 throughput, and rent the H200 when it does not.

When the B200 wins and when it does not

The B200 vs H200 inference choice comes down to whether you can saturate the card. Use these conditions.

The B200 wins when:

• Decode is bandwidth-bound and you run large enough batches to keep the card busy, so the 1.67x bandwidth advantage converts to real tokens per second.
• The model plus KV cache needs more than 141GB, where one B200 replaces two H200s and removes cross-card communication overhead.
• You serve FP4-quantized models, where Blackwell's native low-precision path lifts both prefill and decode.

The H200 wins when:

1. Traffic is bursty and average utilization is low, so the B200 sits idle and its hourly premium is wasted.
2. The model is small (7B to 13B) and a single H200 already runs at acceptable latency below its throughput ceiling.
3. You are latency-sensitive at low concurrency, where neither card is bandwidth-bound and the extra throughput never engages.

Serverless inference and dedicated GPU clusters split along the same line. Serverless is best for variable, API-driven traffic where you should not reason about which card you land on. Dedicated clusters are better when you have profiled your workload and want to pin the B200 deliberately because you can keep it saturated.

Running the B200 in production on GMI Cloud

A throughput advantage only shows up on your invoice if the platform delivers the full bandwidth the card advertises. GMI Cloud's bare metal B200 and H200 instances run with no hypervisor, delivering 100% of the advertised memory bandwidth (8.0 TB/s on the B200 and 4.80 TB/s on the H200) to your workload, because virtualized environments commonly skim throughput before the GPU sees your traffic. For a bandwidth-bound decode loop, that skim is the difference between clearing the 1.54x break-even and falling short of it.

GMI Cloud is optimized specifically for AI inference, with NVIDIA Reference Architecture validation and a 99.99% platform availability SLA. Bare metal B200 instances ship with root access and a preconfigured stack including recent CUDA, TensorRT-LLM, and vLLM, so the time from provisioning to first token is short and you can start measuring real throughput immediately.

GMI Cloud is best suited for teams that have profiled an inference workload, confirmed it is bandwidth-bound at production batch sizes, and want the B200's throughput without hypervisor overhead eroding it.

• Best for: large or FP4 models where one B200 replaces multiple H200s and removes sharding overhead.
• Best for: sustained, high-concurrency decode that can saturate 8.0 TB/s.
• Not ideal for: small models or bursty traffic that leave the B200 underutilized, where the H200 or serverless is cheaper.

To verify current rates and instance configurations before you commit, the live numbers are on the GMI Cloud pricing page, and deployment details are documented at docs.gmicloud.ai.

Measure Throughput First, Then Decide on the B200

The B200 is the cheaper card in production exactly when it produces more than 1.54x the tokens per second of an H200, and the more expensive mistake when it does not. Before you compare hourly rates, run your actual model on both cards at production batch sizes and read the cost per token, not the cost per hour. If the B200 clears the break-even, it lowers your bill while raising your throughput ceiling. If it does not, the H200 keeps your cost per token lower and leaves the premium in your budget. Profile the workload, do the division, then rent the card the math points to.