NVIDIA B200 vs H100 in 2026: why the pricier GPU is cheaper per token

Summary. In MLPerf Inference v6.0 (April 2026), a single NVIDIA B200 pushed about 17,500 tokens per second on Llama 2 70B in offline mode, against roughly 3,000 for an H100, a 5.8x throughput gap. The B200 rents higher: about $6.69 per hour on Lambda in July 2026 versus $2.49 for an H100, a 2.7x premium. Divide cost by output and the ranking flips. At those rates the B200 works out near $0.106 per million tokens and the H100 near $0.231, so the more expensive chip is roughly 2.2x cheaper per token. This guide shows the arithmetic, compares the two GPUs on specs and cloud price across eight providers, and marks the workloads where an H100 is still the correct buy in 2026.

The instinct to rent the cheaper GPU per hour is the most common way engineering teams overpay for inference. Hourly price is an input cost; tokens produced is the output; only the ratio of the two is a real unit cost. A B200 draws 1,000 watts, needs a top-tier chassis, and NVIDIA dropped the PCIe form factor for Blackwell in the data center, so it is harder to get and pricier to run. It still wins on cost per token for high-throughput inference because its fifth-generation Tensor cores, 192 GB of HBM3e, up to 8 TB/s of memory bandwidth, and native FP4 quantization move far more tokens per rented hour.

That does not make the H100 obsolete. It remains broadly available, battle-tested, and cheaper per hour, and for small models, memory-bound workloads, and bursty traffic it is often the better economic choice. The decision is not "newest is best," it is "match the unit cost to the workload." Below is the data to do that, current as of July 2026.

B200 vs H100: specs and cloud price

The B200 carries 2.4x the memory and about 3.9x the bandwidth of an H100, and moves roughly 5.8x the tokens per second on the Llama 2 70B offline benchmark. The B200's MSRP was around $30,000 to $40,000 in clusters of eight or more, close to the H100's own $25,000 to $40,000 band, so on a per-chip basis the gap is narrower than the hourly cloud premium suggests. The hourly spread is driven less by silicon cost than by scarcity: Blackwell supply has not caught up with demand, and most B200 capacity sits behind enterprise contracts and waitlists.

The per-token math, shown in full

The unit that matters for inference is cost per million tokens, and it is a two-line calculation.

First, tokens produced per hour equals throughput in tokens per second times 3,600. Second, cost per million tokens equals the hourly rate divided by tokens-per-hour, times one million. Using the MLPerf Inference v6.0 offline throughput for Llama 2 70B and Lambda's July 2026 on-demand rates:

• H100: 3,000 tokens/sec times 3,600 is 10.8 million tokens per hour. At $2.49 per hour, that is $0.231 per million tokens.

• B200: 17,500 tokens/sec times 3,600 is 63 million tokens per hour. At $6.69 per hour, that is $0.106 per million tokens.

The B200 costs 2.7x more per hour and produces 5.8x more tokens, so its cost per token lands at roughly 46% of the H100's. The premium hourly rate is real, but throughput outruns it. This is the same discipline we applied to precision tuning in our H100 FP8 vs BF16 inference cost breakdown: the sticker rate is never the unit cost.

Cost per million tokens across providers

Cloud rates vary widely, so the B200 advantage widens or narrows by provider. The table derives cost per million tokens from each provider's July 2026 B200 hourly rate and the MLPerf v6.0 offline figure of 17,500 tokens/sec, alongside an H100 baseline at $2.49/hr and 3,000 tokens/sec.

Two lessons fall out. On specialist GPU clouds such as Vultr, Hyperbolic, RunPod, and Lambda, the B200 is 2x to 4x cheaper per token than an H100. On hyperscaler on-demand pricing the advantage collapses: an AWS B200 barely beats a Lambda H100, and an Azure B200 on-demand is actually more expensive per token than the H100 baseline. Where you rent changes the answer as much as which chip you rent.

These per-token figures are derived, not measured on your workload. Real throughput moves with batch size, sequence length, quantization (FP4 versus FP8 or BF16), and the serving stack, so treat the numbers as a planning baseline and benchmark your own model before committing capacity.

What actually drives the B200 advantage

The throughput leap is not raw clock speed. Native FP4 support on Blackwell lets the B200 run inference at four-bit precision where Hopper tops out at FP8, roughly doubling effective compute for models that tolerate the lower precision. Pair that with 8 TB/s of memory bandwidth, nearly four times the H100, and the B200 keeps its tensor cores fed on large models where the H100 stalls waiting on memory. NVIDIA credits FP4 and bandwidth as the primary sources of Blackwell's MLPerf gains.

The catch is that FP4 is not free. Quantizing to four bits can cost accuracy on some tasks, and the gains assume a serving stack tuned for Blackwell. A team that lifts an FP16 H100 pipeline onto a B200 without re-quantizing will see a fraction of the benchmark advantage and still pay the premium hourly rate. The hardware sets the ceiling; the software decides how close you get to it.

When the H100 is still the right buy

Cheaper per token at high throughput does not mean cheaper for every job. The H100 wins in several common cases.

Carl Peterson, writing for the GPU cloud Thunder Compute in July 2026, put the case plainly: "The NVIDIA H100 is not a compromise. It's a proven, widely available GPU that handles the vast majority of real-world AI workloads." The economics back that up in four situations.

Small and mid models that fit comfortably in 80 GB do not need the B200's memory or bandwidth, so the H100's lower hourly rate wins. Bursty or low-utilization inference, where a GPU sits idle much of the time, favors the cheaper hourly rate because you are paying for wall-clock hours, not tokens. Availability matters too: B200 capacity is waitlisted and enterprise-gated, while H100s are rentable on demand across dozens of clouds today. And for teams not yet running FP4, the B200's headline advantage is unrealized, so the H100 is the rational hold until the serving stack is ready.

Decision guide by workload

India-specific considerations

For teams building in India, three factors reshape the math. At about ₹96.4 to the US dollar in mid-July 2026, a Lambda B200 hour is roughly ₹645 and an H100 hour about ₹240, so the absolute rupee outlay is real even when the per-token cost favors the B200 at scale. The break-even depends on utilization: the B200 only earns its premium when you keep it busy enough to convert throughput into tokens.

Sovereign capacity changes the picture further. The IndiaAI Mission has moved to subsidize tens of thousands of GPUs for local developers, with reported effective rates far below commercial cloud, which can make domestic Blackwell and Hopper capacity cheaper than global on-demand pricing for eligible teams. We covered the programme in our IndiaAI Mission and sovereign AI analysis. Data residency is the third factor: workloads touching personal data fall under the Digital Personal Data Protection Act, 2023, which can push teams toward India-hosted GPU capacity regardless of the per-token rate. Teams modelling broader spend can cross-read our GPT-5.6 enterprise inference cost analysis and our AWS GPU capacity and price breakdown.

FAQ

How eCorpIT can help

eCorpIT helps engineering teams size and cost LLM inference before they commit to GPU capacity. We benchmark your actual models on H100 and B200 instances, calculate real cost per token across providers, and design the quantization and autoscaling setup that keeps unit cost predictable, including India-hosted options where DPDP residency matters. If you are choosing between Hopper and Blackwell or trying to cut an inference bill, talk to our engineering team for a workload-specific recommendation with indicative cost and timeline.

References

1. Thunder Compute: NVIDIA B200 pricing (July 2026)

1. Thunder Compute: NVIDIA H100 pricing (July 2026)

1. Spheron: NVIDIA B200 specs and benchmarks, 17,500 tok/s on Llama 70B

1. NVIDIA: Blackwell delivers massive performance leaps in MLPerf Inference

1. IntuitionLabs: H100 rental prices compared across 15+ clouds (2026)

1. Epoch AI: B200 cost breakdown

1. getdeploying: H100 cloud pricing across 48+ providers (2026)

1. Lyceum Technology: LLM inference tokens per second, 2026 benchmarks

1. Lambda: MLPerf Inference v5.1, HGX B200 for enterprise inference

1. ValueAdd VC: India's AI policy 2026, GPU procurement and startup support

1. Exchange Rates UK: USD to INR history, 2026

_Last updated: 19 July 2026._